The workflow you already wrote is probably enough

Earlier this month, I went looking for a prompt workflow that I knew I had written. That’s usually a bad sign. The workflow already existed, the thinking was solid, and the structure still made sense, but I still had to find the post, copy the correct pieces, paste them into Claude, and move the outputs somewhere else.

That’s where most prompt libraries start to break down. The prompt usually holds up. The operating model around it breaks first, because a good workflow trapped in a post, Google Doc, or Notes still asks you to coordinate too much by hand.

I have more than 100 prompt workflows sitting inside the TinyTechGuides inventory. Some are useful references. A smaller number deserve promotion into something I can run without hunting through old posts. For this tutorial, I’m using one of the buying-committee workflows from the lead magnet project, Hidden Objection and Risk Surface Analysis. It already has the bones of a Skill because it defines the inputs, follows a repeatable process, and gives a PMM or sales team an output worth running more than once.

The goal here is not enterprise automation nirvana. I want to take one workflow that already works, package it so Claude can run it cleanly, and make the next run easier than the last one. The thinking is already done, so the job is to stop rebuilding it by hand every time.

The example: hidden objections

The Hidden Objection workflow is built for a problem that most PMMs recognize. Sales teams prepare for the objections that buyers say out loud, such as price, timing, integrations, or missing features. The deals that disappear into silence usually die somewhere else, when someone sees career risk, political risk, implementation risk, or budget risk and decides that doing nothing feels safer.

That makes it a strong workflow for this exercise. It is not a one-line prompt that asks Claude to brainstorm objections. The analysis starts with inputs, moves through six steps, and ends with a risk surface map that marketing and sales can use. It also asks for the same inputs that a Skill should collect, such as product, market, deal size, committee context, sales notes, and win-loss data.

The steps already behave like a process. It surfaces hidden objections by stakeholder role, maps the “no decision” incentive structure, looks for organizational risk triggers, and builds a mitigation plan. A final pass turns the analysis into content recommendations and a usable sales-facing deliverable.

What makes this workflow convertible

Not every prompt workflow deserves to become a Skill. Some prompts are better left as one-off thinking aids. A naming brainstorm or rough first-draft helper probably does not need a folder, a command name, and a maintained instruction file.

The workflows that deserve conversion have a few traits in common. You run them more than once, they ask for structured inputs, and their output needs to look consistent every time. They also carry enough judgment that you do not want each user improvising the process from scratch.

The Hidden Objection workflow passes that test. A slash command could be /hidden-objection-analysis. Its required inputs already live in Step 0, the reasoning path follows the six-step sequence, and the output can always include a risk surface analysis, content recommendations, and a short executive summary.

Expectations matter as much as those criteria. A first Skill does not need to connect to Salesforce, Gong, and every other system in the company on day one. The first version should do what the original workflow already did, with fewer chances to lose the thread.

Strip the workflow down to its operating parts

The easiest way to convert a workflow is to stop treating it as prose. An article explains the idea and gives the reader context. A Skill needs the operating parts.

In this workflow, the operating parts are easy to spot. Step 1 asks Claude to act as a senior B2B sales psychologist and buying committee analyst. It gives the business context, then Claude identifies the surface objection, hidden objection, root fear, behavioral signal, and trigger event for each stakeholder.

That is the old prompt structure hiding in plain sight. Role. Context. Task. Format. Tone. I still like that structure because it forces the hard thinking before the model starts generating, but it is not the finished product.

For a Skill, those pieces become reusable instruction blocks. The role gives the Skill its perspective, and the context becomes required inputs. The task becomes the process. Format becomes the output contract, while tone keeps the analysis direct instead of generic.

Each part has a direct translation:

- Role: who the Skill should act as

- Context: the inputs the Skill must collect before running

- Task: the ordered process the Skill should follow

- Format: the required output sections and tables

- Tone: the quality standard for the finished deliverable

That mapping is most of the conversion. Separate durable operating instructions from one-time article copy, then make those instructions easy for Claude to invoke.

Turn those parts into SKILL.md

Claude Code Skills are built around a SKILL.md file. Anthropic describes Skills as reusable instructions that Claude can load when relevant or when you invoke them directly with a slash command.^[1] The file does not need to be complicated. It needs to tell Claude when to use the Skill, what inputs it needs, what process to follow, and what output to produce.

For the Hidden Objection workflow, a finished SKILL.md looks like this:

description: Build a hidden-objection risk surface analysis for a B2B buying committee. Use when the user wants to diagnose why enterprise deals stall, go quiet, or end in no decision.

argument-hint: “[company] [product] [solution_category]”

---

## Required inputs

Before running, collect or infer:

- Company and product

- Solution category

- Target industry and target organization size

- Typical deal size

- Existing personas or committee map

- Sales observations, win-loss notes, or stalled-deal patterns

If any required input is missing, ask for it before producing the analysis.

## Process

1. Identify the likely buying committee roles.

2. Surface hidden objections by stakeholder role.

3. Map the no-decision incentive structure.

4. Identify urgency accelerators and risk amplifiers.

5. Build a risk mitigation plan.

6. Recommend content and messaging that preempts the highest-risk objections.

## Output format

Return:

1. Executive summary

2. Hidden-objection table by stakeholder

3. No-decision incentive analysis

4. Risk trigger map

5. Mitigation plan

6. Content and messaging recommendations

## Quality bar

Write like a senior B2B product marketing advisor. Be candid, specific, and grounded in buying-committee behavior. Do not produce generic persona language.

That snippet is the compressed operating system for the workflow. The original prompt doc can still live as a reference file, especially if you want Claude to load the full step-by-step version when needed. The main SKILL.md should stay concise because the loaded Skill text stays in context once the Skill runs.^[2] You can type that file out by hand, but it is faster to have Claude draft it from the workflow you already wrote, then check the draft against the shape above.

Have Claude draft the first version

Paste the prompt workflow into Claude and ask it to produce that structure for you:

I want to convert this prompt workflow into a Claude Skill.

Review the workflow below and create:

1. A concise `SKILL.md`

2. A clear description and argument hint

3. Required inputs

4. Core process steps

5. Expected output format

6. Quality rules

7. Any reference files that should sit beside the Skill instead of inside `SKILL.md`

8. A recommendation on whether this should be one Skill, several smaller Skills, or one orchestration Skill that points to companion Skills

Do not copy the workflow directly. Compress it into durable operating instructions.

Here is the workflow:

[PASTE WORKFLOW]

The first draft will probably be too literal, and that is fine. Ask Claude which parts are durable instructions, which details belong in a reference file, what inputs are required, where the Skill should ask clarifying questions, which steps can be combined, and what tends to break.

Atomization is the judgment call here. Do not split a workflow into separate Skills just because the original article had separate steps. Split it only when a subtask can stand alone and will be reused in other workflows. In the PMM stack, buying committee mapping, hidden-objection analysis, and committee-aware messaging could each become separate Skills over time, with a higher-level Skill telling Claude when to use those companion workflows and how to assemble the final deliverable.

Portability starts to matter once the workflow becomes part of the operating system. I am using Claude because it is where I run this marketing stack today. Codex and Gemini CLI now support agent skills built around SKILL.md, and both have project-context files that play a similar role to CLAUDE.md.^[3] The names change by tool, but the operating idea holds. Repeated instructions belong in reusable workflow files, not in a prompt you keep pasting from a browser tab.

What changes when it becomes a Skill

You feel the difference the first time you run it. A prompt workflow says, “Here is how to do the work.” A Skill says, “Run this job.” With the old workflow, you open the source post, copy the prompt, paste the inputs, run the step, move the output forward, and keep going until the final analysis comes together.

With the Skill, the orchestration moves into the instruction file. You type something like /hidden-objection-analysis “Acme Analytics” “Decision intelligence platform” “enterprise analytics” and Claude knows what job to run. It can ask for missing inputs and return the output in the agreed format.

Each manual step has a Skill equivalent:

- A prompt workflow step becomes a SKILL.md instruction

- Pasting variables by hand becomes a required-input list

- Copying outputs across steps becomes a Skill-managed process

- A final doc assembled by hand becomes a standard output format

- A one-off chat run becomes a reusable slash command

The Skill gets better when it reads the rest of the project context. CLAUDE.md can tell it the company positioning, voice rules, output conventions, and where source files live. Memory can preserve recurring preferences. MCP connectors can eventually pull sales notes or account context instead of asking you to paste everything into chat.^[4]

You do not need all of that on day one. The first useful version removes the copy-paste coordination. Later versions can read project context or reach into tools when the workflow deserves that extra plumbing.

The conversion pass I’d run first

If I were converting this workflow in a client project, I would start with one focused pass and stop when the Skill can produce a credible first output. The perfect version can wait.

1. Pick the slash command name. Use a verb phrase that describes the job, not the source document. /hidden-objection-analysis is better than /workflow-10 because it names the problem.

2. Pull the Step 0 variables into a required-input list. The workflow already has the correct inputs; they just live in article form instead of an input contract.

3. Compress the six workflow steps into process instructions. Keep the order, but cut the repeated setup. The Skill does not need six separate role blocks.

4. Lock the output format. For this workflow, I would require an executive summary, a stakeholder table, a no-decision analysis, a risk trigger map, and content recommendations. Readers trust the Skill when the shape stays stable.

5. Test it on one real stalled deal. A real opportunity exposes whether the Skill asks for the right inputs, notices the right stakeholder risks, and produces something sales would use.

That is the first 30 minutes. The next pass can add examples, reference files, scripts, or MCP access. Aim first for a repeatable instruction file that runs without you babysitting each step.

Promote the best workflows

Prompt workflows were the right unit for the first wave of AI marketing work. They forced marketers to define the role, context, task, format, and tone before asking the model to produce anything, and that discipline still matters.

The useful workflows now need a promotion path. Some can stay as posts, some belong in a gated PDF, and a smaller number should become Skills because they represent work you want to run repeatedly and improve over time. That last group is why the lead magnet matters. The PMM’s Prompt Playbook gives readers 11 workflows that solve real product marketing problems, and the next step is to choose the first one to convert, name the command, and make it easier to run the second time.

Start with the workflow that already gets reused, package it as a Skill, then run it once, fix what breaks, and let the next run inherit the improvement.

The prompt library was the raw material. The Skill is the operating procedure.

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Frequently asked questions

What is a Claude Skill?

A Claude Skill is a reusable instruction folder that Claude Code can load when relevant or when you call it with a slash command. The core file is usually SKILL.md, which tells Claude when to use the Skill, what inputs it needs, what process to follow, and what output to produce. For marketers, a Skill turns a repeatable prompt workflow into something closer to an operating procedure, part of the broader move from chatbots toward agentic AI systems that plan work and act across tools.^[5]

How do I convert a prompt workflow into a Claude Skill?

Paste the existing workflow into Claude and ask it to draft a concise SKILL.md with a description, argument hint, required inputs, process steps, output format, quality rules, and supporting reference files. Claude will often copy too much from the source on the first pass, so your job is to compress it into durable instructions and test it on a real example.

Should every prompt workflow become a Skill?

No. Convert workflows that you run more than once, require structured inputs, produce a stable output, and carry judgment that should not be reinvented each time. A brainstorming prompt, summary helper, or rough first-draft prompt can stay in your library.

Should one workflow become one Skill or several Skills?

Start with one Skill unless a subtask can stand on its own and will be reused elsewhere. Splitting a six-step article into six Skills usually creates more overhead than value. In a PMM stack, buying committee mapping, hidden-objection analysis, and committee-aware messaging could each support other workflows, so a higher-level Skill can orchestrate them as companions.

Do Codex and Gemini CLI support a similar idea?

Yes. Codex and Gemini CLI both support agent skills built around SKILL.md, and both have project-context files that work like a project rulebook. The file names and implementation details differ, but the operating principle is the same. Repeated instructions belong in reusable workflow files that the agent can load when needed.

What should I ask Claude after it drafts the Skill?

Ask which instructions are durable, which details should move into a reference file, what inputs are required, where the Skill should ask clarifying questions, which steps can be combined, what tends to break, and how to test it on a real stalled deal. The first draft is a starting point, not the final asset.

About David Sweenor

David Sweenor is a Top 25 AI thought leader, author, and founder of TinyTechGuides. He spent the first half of his career as a practitioner at IBM in data science, business intelligence, and data warehousing. The second half he led product marketing teams at SAS, Dell Software, Quest, TIBCO, Alteryx, and Alation across advanced analytics, AI, and B2B marketing transformation. He writes about AI for marketers, Claude Skills, prompt workflows, and B2B operator depth at TinyTechGuides.

Books

- Artificial Intelligence: An Executive Guide

- Generative AI Business Applications

- The Generative AI Practitioner’s Guide

- The CIO’s Guide to Adopting Generative AI

- Modern B2B Marketing

- The PMM’s Prompt Playbook

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Anthropic. “Extend Claude with skills.” Claude Code Docs. Accessed June 16, 2026. https://docs.anthropic.com/en/docs/claude-code/skills.

^[2]Anthropic. “Extend Claude with skills.” Claude Code Docs. Accessed June 16, 2026. https://docs.anthropic.com/en/docs/claude-code/skills.

^[3]OpenAI. “Agent Skills.” Codex Manual. Accessed June 16, 2026. https://developers.openai.com/codex/codex-manual.md. Google. “Agent Skills.” Gemini CLI Docs. Accessed June 16, 2026. https://geminicli.com/docs/cli/skills/. Google. “Provide context with GEMINI.md files.” Gemini CLI Docs. Accessed June 16, 2026. https://geminicli.com/docs/cli/gemini-md/.

^[4]Anthropic. “Connect Claude Code to tools via MCP.” Claude Code Docs. Accessed June 16, 2026. https://docs.anthropic.com/en/docs/claude-code/mcp.

^[5]Purdy, Mark. “What Is Agentic AI, and How Will It Change Work?” Harvard Business Review, December 12, 2024. https://hbr.org/2024/12/what-is-agentic-ai-and-how-will-it-change-work.

Last Tuesday, I was chatting with Claude Code and wanted to write a new B2B messaging prompt workflow.^[1] Then I vaguely recalled writing one last year, so I searched Substack and realized I had a couple of them sitting there since 2025, just waiting and wanting to be used again.

That caught me. When I’m going in circles with Claude Code, Gemini CLI, or Codex, my first inclination is to write yet another prompt workflow. Most marketers I know do the same thing, and that instinct was right a year ago. It isn’t anymore.

I’ve published more than 100 articles on AI, marketing, and prompt workflows on insights.tinytechguides.com since early 2025. Each one was useful the day it went live. Most of them are sitting in the inventory right now, ready for reuse, and many of them are doing more than that. They’ve formed the basis for the 60+ Claude Skills I use regularly today.

Two weeks ago, I wrote about the four-component stack that makes Claude reliable for marketing work.^[2] Skills do the work, and three supporting pieces, CLAUDE.md, memory, and MCPs, make the Skills compound. That post explained why each component, on its own, falls apart. This post answers the question I kept getting in the inbox afterward.

You don’t need more prompt workflows. You need to package, refine, and continually improve the ones you have. The conversion is faster than writing a new workflow from scratch, and it’s what the rest of this piece is about. On the continual improvement side, the /reflect skill and napkin.md are for a future post.

A prompt workflow is a one-shot, whereas a Skill compounds.

A prompt workflow is linear. You paste it into a chat window, fill in the variables, and run it.^[3] You copy the output somewhere useful and close the tab. Run the same workflow next month, and it forgets everything that you fixed last time. The model has no memory of which variant you settled on, which phrasings you cut, or which output format your client preferred. Was it a deck? A Google doc or a webpage?

Stack a hundred of those together, and you have a prompt library. A library is the right starting point. Each entry was useful the day that you wrote it, and each one is one trigger away from working again. Unfortunately, none of them know about each other’s capabilities, and the library essentially acts as a drawer full of sticky notes.

“A library of prompt workflows is dormant inventory. A library of Skills compounds every time you use it.”

— David Sweenor, Founder/CEO, TinyTechGuides

When most of us started writing prompt workflows, Claude Skills barely existed, and we were still enamored with ChatGPT. Everyone was talking about prompt libraries, prompt engineering, organizational context, and Custom GPTs. We wrote workflows, reused them, and shared Custom GPTs. That was the toolkit at the time. The tech has evolved, and the work hasn’t been lost. Every saved prompt workflow is raw material for a Skill.

A Skill is a different unit, a folder that Claude loads when you invoke it with a slash command. Loaded into a project that has its own CLAUDE.md, its own memory folder, and a few MCP connectors, the Skill stops being a recipe in a drawer. It becomes a recipe in a kitchen, with a smart pantry that knows your preferences and appliances wired into your accounts. The next time you run it, the kitchen remembers the corrections that you made the last time.

The next ten workflows I write won’t move the needle, but the ones I convert into skills will.

What a Skill adds that a prompt workflow can’t

A prompt workflow is a recipe. A Skill is a recipe plus three things a recipe alone never had.

The first is a trigger. A workflow lives in a doc somewhere. To run it, you have to find the doc, copy the prompts, paste them into a chat window, and orchestrate the steps yourself. Custom GPTs are a little better. You can fire one off without having to hunt for the doc. They’re a pain in the butt to update, though, because every tweak means logging back into the GPT builder to edit the instructions and hoping nothing else broke. A Skill solves that update problem because every correction lands in the conversation, not in a separate builder window.

A Skill is a slash command. Type /review-article and the workflow runs. The Skill is a verb you can say to the project, not a script you have to fetch.

The second is inheritance. A Skill reads the project’s CLAUDE.md and your memory folder every time it runs. The variables you filled in by hand were your product name, your client’s industry, and your usual output format. Now, they all come pre-filled from files that already exist. One Skill works across many projects without reconfiguration. Run the same /review-article Skill in two different client projects, and you get two different reviews of the same draft, both correct, because each project’s rulebook carries its own voice.

The third is tool access. A workflow that ends with “now go pull the latest win-loss notes from HubSpot or Salesforce and bring them back” asks you to do half the work. A Skill wired to a Model Context Protocol (MCP) server goes and gets the notes. Per Anthropic’s documentation, Skills are organized folders that load only when relevant, and they live next to the tools they need.^[4]

“A Skill is the recipe that remembers where the pantry is.”

— David Sweenor, Founder/CEO, TinyTechGuides

The second run of a workflow is identical to the first, except for a new set of variables. The second run of a Skill inherits everything that the project learned the first time, and the third run inherits everything from the first two. The work that you put in once does more work each time you call it.

From workflow to Skill

The Strategic Battlecard workflow is one of the more popular ones I’ve published at TinyTechGuides.^[5] It’s a 10-step prompt workflow that produces a single-page battle card for a single competitor. You enter the competitor’s name, your product, and your industry. Then you add the proof points the workflow asks for, such as win themes from recent deals, and Gong call analysis. It’s the kind of doc you reach for when sales complains they don’t know how to position effectively against a competitor.

In its published form, the workflow is a long doc. To run it for a new competitor, you copy each prompt into a chat window in order and fill in the variables by hand, then copy the output between steps and stitch the final battlecard together yourself. About 40 minutes of orchestration on top of the 20 minutes of strategic thinking that the workflow is supposed to produce.

The same workflow as a /build-battlecard Skill takes about 4 minutes, and the Skill has grown beyond what the published version does. I type two arguments at runtime, the client name and the competitor name, and the Skill does the rest. It reads the client’s CLAUDE.md and messaging files, so positioning and differentiators come pre-filled. From there, it pulls fresh competitor intel from a /competitive-site-monitor Skill that runs on a schedule, plus win rate and pipeline-at-risk numbers from a /run-win-loss-analysis Skill that ran the last quarter’s deals. None of those inputs existed in the original workflow because it asked you to paste them in by hand.

When the Skill finishes, it writes two output files (a 2-page AE quick reference and a detailed deal-strategy version) and pushes both into Notion under the Competitive Center. Then it updates a tracker database (or spreadsheet) so the competitive coverage status stays honest. The published workflow produces one doc. The Skill produces a competitive system.

Converting a workflow into a Skill doesn’t require you to learn SKILL.md syntax. Open Claude and paste it into your workflow, then ask it to convert it into a Skill you can call with a slash command. Or sketch what you want the Skill to do, then paste the existing workflow as source material. Either direction lands you in roughly the same place. Save the file that Claude generates into .claude/skills/, and you’re done. If you’re migrating from ChatGPT Custom GPTs, the same path works. Paste the GPT’s instructions instead of a workflow doc and ask Claude the same question.

The next time you run the Skill, you can tweak it on the fly. Spot a phrase that you don’t like in the output? Tell Claude in the same window. Want a different output format for this client? Mention it. The Skill picks up the correction, and the next run inherits it. A workflow in a doc can’t do that, because every edit is a manual re-save.

I’ll walk through the line-by-line conversion in a future post. The pattern repeats across every Skill I’ve built. My /build-content-calendar Skill grew the same way. It started as a workflow doc. Now it reads the canonical Google Sheet through an MCP and writes the next week’s content row without me touching the chat window.

Pick ten. Don’t write any new ones this month.

If you’ve got a bunch of workflows you’ve written over the past year or two, you don’t need to convert all of them. Pick ten and let the rest stay in the inventory until you need them. Four criteria help you decide which ten go first.

1. The one you’ve run more than five times: Reuse signal beats novelty signal. The workflow that you keep coming back to is the workflow that pays off when it compounds.

2. The one that takes the most copy-paste orchestration to run: The more steps between typing the prompt and getting the output, the more the conversion saves you per run.

3. The one your team or your clients also run: Shared infrastructure beats individual heroics. A Skill that runs the same way for three people is worth converting three times faster than a Skill that only one person uses.

4. The one that touches a system you’d rather not export data from: The workflow that ends with “now go pull the latest deal notes from Gong” or “paste in the last week’s web traffic from GA4” is the workflow with the highest MCP payoff.

“Ten Skills running across three clients is thirty workflow runs a week. The library becomes the process moat.”

— David Sweenor, Founder/CEO, TinyTechGuides

The ten I convert this quarter become infrastructure. The other workflows wait in the inventory, fine where they are, ready for the next time I need one. If you’re stuck on where to start, pick the workflow tied to the angle that your readers can’t get anywhere else. The ten you pick are the ones that turn your library into a process moat.^[6]

The library you already have

Your library of prompt workflows is one conversion away from compounding. The next workflow you write can wait. Pick ten of the ones that you’ve already published and start there.

The full inventory of more than 100 prompt workflows lives at insights.tinytechguides.com. Pick ten this month and convert them. Don’t want to be writing new prompts six months from now? Subscribe, and the next post lands in your inbox.

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Frequently asked questions

What’s the difference between a prompt workflow and a Claude Skill?

A prompt workflow is a linear sequence of prompts that you paste into a chat window, fill in variables for, and run step by step. A Claude Skill is the same workflow packaged into a folder that Claude can invoke with a single slash command. Skills inherit context from the project’s CLAUDE.md and your memory folder, pull data through MCP servers, and pick up corrections from one run to the next. A prompt workflow is one-shot; a Skill compounds.

What is a Claude Skill?

A Claude Skill is a folder of instructions, scripts, and resources that Claude loads when you invoke it with a slash command. Per Anthropic’s documentation, Skills are organized folders that load only when relevant. A Skill typically contains a SKILL.md file describing what the Skill does, optional templates or examples, and pointers to the MCP servers it needs. When you call a Skill in a project, Claude reads the project’s CLAUDE.md and memory folder to fill in context before running the workflow.

How do I convert a prompt workflow into a Skill?

Open Claude, paste your existing workflow, and ask Claude to turn it into a Skill you can call with a slash command. Or start with a sketch of what you want the Skill to do, then paste the workflow in as source material. Either direction lands you in roughly the same place. Save the file that Claude generates into your project’s .claude/skills/ folder. You don’t need to learn SKILL.md syntax by heart; Claude builds the file structure for you.

Which prompt workflows should I convert to Skills first?

Four criteria help you pick. First, the one you’ve run more than five times, because the reuse signal beats the novelty signal. Second, the one that takes the most copy-paste orchestration to run, because the bigger the orchestration, the more the conversion saves per run. Third, the one your team or your clients also run, because shared infrastructure beats individual heroics. Fourth, the one that touches a system you’d rather not export data from manually, because that’s where the MCP payoff is highest.

How are Claude Skills different from Custom GPTs?

Custom GPTs improved on prompt docs by letting you trigger a workflow without needing to find the source file. Skills go further. A Skill reads your project’s CLAUDE.md and memory folder every time it runs, so variables come pre-filled per project. A Skill can call the MCP servers to pull data from Gmail, Sheets, or a CRM. When you spot something you want to change mid-run, you tell Claude in the same window, and the Skill picks up the correction. Updating a Custom GPT requires logging back into the GPT builder.

Why is a library of Skills a process moat?

A library of prompt workflows is dormant inventory. Each entry is one trigger away from working, but nothing compounds across runs. A library of Skills is a process infrastructure. Each Skill inherits from CLAUDE.md and memory, calls tools via MCPs, and carries over corrections into the next run. Ten Skills running across three clients produce thirty workflow runs a week without writing new prompts. The library becomes Hamilton Helmer’s Process Power applied to a marketing function.

About David Sweenor

David Sweenor is a Top 25 AI thought leader, author, and founder of TinyTechGuides. He spent the first half of his career as a practitioner at IBM, working in data science, business intelligence, and data warehousing. In the second half, he led product marketing teams at SAS, Dell Software, Quest, TIBCO, Alteryx, and Alation, covering advanced analytics, AI, and B2B marketing transformation. He writes about AI for marketers, Claude Skills, prompt workflows, and B2B operator depth at TinyTechGuides.

Books

- Artificial Intelligence: An Executive Guide

- Generative AI Business Applications

- The Generative AI Practitioner’s Guide

- The CIO’s Guide to Adopting Generative AI

- Modern B2B Marketing

- The PMM’s Prompt Playbook

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Sweenor, David. “The Marketer’s Case for Claude Code.” TinyTechGuides, May 8, 2026. https://tinytechguides.com/blog/the-marketers-case-for-claude-code/

^[2]Sweenor, David. “The Four Layers of a Claude Stack (and why Skills alone fall apart).” TinyTechGuides, May 13, 2026. https://tinytechguides.com/blog/four-components-claude-stack/

^[3]Sweenor, David. “Marketing Mad Libs: Prompt Variables for Smarter Content Automation.” TinyTechGuides, February 17, 2025. https://tinytechguides.com/blog/marketing-mad-libs-prompt-variables-for-smarter-content-automation/

^[4]Anthropic. “Extend Claude with skills.” Claude Code Documentation, accessed May 15, 2026. https://docs.anthropic.com/en/docs/claude-code/skills.

^[5]Sweenor, David. “Strategic Battlecard Workflow for Competitive Wins.” TinyTechGuides Insights, July 14, 2025. https://insights.tinytechguides.com/p/strategic-battlecard-workflow-for

^[6]Sweenor, David. “The Marketing Moat Nobody Is Talking About in 2026.” TinyTechGuides, May 7, 2026. https://tinytechguides.com/blog/marketing-moat-2026/

When I was writing the last post on the four components of a Claude stack, I went to grab a screenshot of my memory folder and couldn’t find it.^[1] Finder didn’t show .claude and Spotlight returned nothing. For ten minutes, I was frantically trying to figure it out. Did I have Mad Cow? Amnesia? Did Claude and I delete the whole thing when I mindlessly clicked continue when it prompted me to do so, as it was writing some Python code? Fortunately, not, Mac was just hiding it from me.

Anyone who read that post and went looking for the folder may have encountered the same issue. The folder exists, but it’s not in the repo you’re working from or where Finder wants you to look. The Memory component is one of the four pieces that make a Claude marketing OS work, and it lives in a part of your home directory that Mac considers off-limits by default. Finding the folder matters because it’s where everything that compounds across your sessions lives. Claude writes to it, you curate it occasionally, and over time, it holds more of your operating knowledge than any other file on disk.

An earlier post, “The marketer’s case for Claude Code,” covered the install and the four pieces.^[2] The follow-up showed how those pieces compound. This post answers where the Memory component lives and how to open it without the ten minutes of panic that I just spent.

Here’s a five-minute walkthrough to help Mac users overcome some of the “smart defaults” that were put in place to simplify things but end up causing agita. Windows is easier, because File Explorer doesn’t auto-hide files by default. Two simple steps will cure all ills.

Where Claude stores your memory on a Mac

Every project gets its own memory folder inside your home directory. The base path on a Mac is ~/.claude/projects/<encoded-project-path>/memory/. Anthropic provides an easy way to open it without typing the path yourself. From inside Claude Code, type /memory and you’ll get a list of the memory files loaded into your session, along with a link to the folder.^[3] Make sure you select the “Open auto-memory” folder option, which opens the folder in Finder.

But the /memory command doesn’t put the folder in the Cursor’s file tree, which is where I work. Each time I want to read a saved file, running a slash command is one too many steps. There’s a better answer, but first you need to know why Mac hides this folder from you.

Mac hides any file or folder whose name starts with a dot. By default, Finder ignores them, so your user folder doesn’t look like a server room. The Claude folder is called .claude, and Spotlight ignores the same files. That’s why my search for “claude” returned nothing during my ten-minute panic.

Finder will reveal hidden files if you tell it to. Open your home folder, then press Cmd+Shift+. (Command, Shift, and the period key together). Hidden files and folders appear. Press it again to hide them. Inside .claude, you’ll find a projects/ folder, and inside that, a subfolder named after your project’s full filesystem path, with slashes replaced by hyphens. Clunky, but you only need to navigate it once before the next move fixes it for good.

A symlink does the heavy lifting. One terminal command places a shortcut to the memory folder inside your project, so the link shows up at the top of your project’s file tree in Cursor. The full command looks like this:

ln -s ~/.claude/projects/-Users-david-sweenor-Documents-TTG-Advisor/memory ~/Documents/TTG-Advisor/.claude-memory

Replace the paths with your own project name and folder location. Or, if you’d rather not type the command yourself, ask Claude to create the symlink for you! Claude will run it with the correct paths.

A quick aside for the non-terminal natives. If you didn’t start your career at IBM running vi and Emacs on UNIX boxes, you may not have seen this one before. A symbolic link, or symlink, is a UNIX-era trick. The command creates a file that acts like the folder that it points to. Click on the link in Cursor, and you’re inside the memory folder, even though the actual files live in your home directory.

The link is named .claude-memory with a leading dot, so Finder still hides it. Cursor and most editors show dotfiles by default, so the link is visible exactly where you need it. If you’d rather see it in Finder too, drop the dot. Name it claude-memory instead.

Windows readers, the path resolves identically. C:\Users\<you>\.claude\projects\... puts you in the same folder structure. File Explorer doesn’t hide dot-files on Windows, so .claude is visible the moment you open your user folder. Different OS philosophy, same outcome. You can already see it.

What you’ll find inside

Once you’ve found the folder, you have to decide what’s worth putting in it. My TTG memory folder currently holds more than 60 files. Every entry is a decision about what survives across sessions.

The naming convention is a topical prefix followed by a short descriptor: feedback- for working-style rules and corrections, project- for active client work, reference- for pointers to external systems, and topic-specific prefixes like podcast-, wordpress-, and youtube- for tooling notes that don’t fit elsewhere.

Three real entries from my folder:

- feedback-flagged-ai-phrases.md: phrases I keep catching in drafts that need to die (”game-changer,” “shift” as a noun, and “delve”)

- reference-email-signature.md: the exact block to put at the bottom of every outbound email

- <obscured-client-project>.md: what’s active with one of my clients right now, who owns what, and what’s due next

MEMORY.md sits at the root of the folder. It’s the index, and each topic file gets a one-line entry. Claude reads the first 200 lines (or 25KB, whichever comes first) at the start of every session.^[4] The index loads into every conversation; the topic files load only when Claude needs them. Think of MEMORY.md as the table of contents and the topic files as the chapters.

Curation cuts both ways. I don’t save session-specific task state, for instance. I tried it once, and a note about which post I was drafting on a Tuesday in March was useless by Wednesday morning. Now I save what still matters next week.

Memory is the piece that survives

Curation matters only because what you save compounds. Of the four components from the last post, Memory has the longest half-life. Skills run the plays that you trigger. CLAUDE.md captures each project’s rules. MCPs connect Claude to your live tools. Memory does what the others can’t. It carries what you’ve learned across every session, every project, and every conversation that follows.

Claude saves your corrections once. You don’t have to repeat them next session. The preferences that you record stay recorded. Over time, the folder shapes how Claude responds to the specific work that you do. As I’ve written before, teaching AI in your context beats generic automation.^[5]

What’s worth saving and what isn’t is its own conversation. I’ll cover it in a future post on my weekly Claude playbook for B2B marketers. For now, settle for finding the folder and placing a symlink so Finder never traps you again.

Ten minutes of mad-cow panic later, I had my folder back. The harder question is whether I’ll use it once I know where it lives.

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Frequently asked questions

Where does Claude Code store memory files on a Mac?

Claude Code stores memory files at ~/.claude/projects/<encoded-project-path>/memory/. Each project gets its own subdirectory inside .claude/projects/, named after the project’s filesystem path with slashes converted to hyphens. The .claude folder lives in your home directory but is hidden by default because it starts with a dot. The base path resolves to C:\Users\<you>\.claude\projects\... on Windows, where File Explorer shows it without any reveal step. Linux uses the same ~/.claude/projects/... path as Mac, with the same dot-file hiding convention.

Why can’t I see the .claude folder on my Mac?

macOS hides any file or folder whose name starts with a dot. Finder ignores them by default, and Spotlight skips them too. To reveal hidden files, open your home folder in Finder and press Cmd+Shift+. (Command, Shift, and the period key). The .claude folder appears in the file list. Press the same shortcut again to hide hidden files. Windows File Explorer doesn’t apply this convention, so .claude is visible there without any reveal step.

How do I make the Claude memory folder visible inside my project?

A symbolic link works best. From your terminal, run ln -s ~/.claude/projects/<your-encoded-path>/memory ~/Documents/<your-project>/.claude-memory. The symlink appears at the top of your project’s file tree in Cursor and other editors that show dotfiles by default. You can also ask Claude to create the symlink with the right paths substituted. The folder name keeps a leading dot, so Finder still hides it; drop the dot if you want to see it there too.

What is the /memory slash command in Claude Code?

The /memory command shows a list of memory files loaded into your current Claude Code session and provides a link to open the auto memory folder. Run it from within Claude Code, then click the link to launch the folder in Finder. The command works on every operating system that Claude Code supports, including Mac, Windows, and Linux. It’s the official Anthropic way to find the folder without typing the path, but it doesn’t place the folder inside your project’s file tree where you work.

What is MEMORY.md and what gets loaded into a Claude session?

MEMORY.md sits at the root of the auto memory folder and acts as the index. Each topic file in the folder gets a one-line entry in MEMORY.md. At the start of every Claude Code session, Claude reads the first 200 lines (or 25KB, whichever comes first) of MEMORY.md. The topic files themselves load only when Claude needs them. Think of MEMORY.md as the table of contents and the topic files as the chapters Claude pulls when relevant.

What’s the easiest way to open Claude Code’s memory folder?

Three paths work. Run /memory from inside Claude Code and click the link to launch the folder in Finder. That’s the fastest one-off method. To browse manually, open Finder, press Cmd+Shift+. to reveal hidden files, and navigate to ~/.claude/projects/<your-project>/memory/. For daily access while editing, create a symbolic link within your project that points to the folder so it appears in the Cursor’s file tree. The /memory command works on every operating system; the symlink is most useful when you live inside an editor like Cursor.

About David Sweenor

David Sweenor is a Top 25 AI thought leader, author, and founder of TinyTechGuides. He spent the first half of his career as a data practitioner at IBM, working in data science, business intelligence, and data warehousing, and the second half in product marketing leadership at SAS, Dell, Quest, TIBCO, Alteryx, and Alation. His writing focuses on the practical intersection of AI, analytics, and B2B marketing.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications

- The Generative AI Practitioner’s Guide

- The CIO’s Guide to Adopting Generative AI

- Modern B2B Marketing

- The PMM’s Prompt Playbook

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Sweenor, David. “Is your Claude marketing OS a little quirky?” TinyTechGuides, May 13, 2026. https://tinytechguides.com/blog/four-components-claude-stack/

^[2]Sweenor, David. “The marketer’s case for Claude Code.” TinyTechGuides, May 8, 2026. https://tinytechguides.com/blog/the-marketers-case-for-claude-code/

^[3]Anthropic. “How Claude remembers your project.” Claude Code documentation. Accessed May 13, 2026. https://code.claude.com/docs/en/memory

^[4]Anthropic. “How Claude remembers your project.” Claude Code documentation. Accessed May 13, 2026. https://code.claude.com/docs/en/memory

^[5]Sweenor, David. “The Barcode on the Bronze: Why Your AI Needs to Know What Makes You Different.” TinyTechGuides, November 18, 2025. https://tinytechguides.com/blog/the-barcode-on-the-bronze-why-your-ai-needs-to-know-what-makes-you-different/

Many of my clients are gravitating towards Claude Code for marketing. This is surely a step in the right direction. However, most of them are not widely using the Skills that you can create with Claude. In fact, I’ve only recently understood their power and how to use them to their fullest extent. I’ve built a /podcast-prep skill, a /podcast-production skill, a /send-invoice skill, along with many others. In total, I’ve created 60+ of them across two working folders, one for my TinyTechGuides business and another for client work. When I first started using skills, they mostly worked, but at times, they sometimes didn’t consistently do what I wanted. Little did I know their true power.

One of my skills is /build-content-calendar, which is pretty effective. Unfortunately, after using it extensively, I started noticing something off. I’d kept the content calendar in two places, a local CSV and a Google Sheet. Some skills updated the CSV, while others updated the Sheet. When I noticed the discrepancies, I would ask Claude to sync or fix them by hand and move on. A few days later, the two files were out of sync again. The skills had run fine both times. They each edited whichever file they knew about. Turns out, I broke a cardinal rule: one source of truth — I should have had the local copy simply reference the Google Sheet version.

“I fixed it. A few days later, it was broken again.”

The same week, I caught my /podcast-prep skill churning out question lists with em-dashes I’d banned, and it kept opening with generic “tell us about yourself” questions even though I’d told it to always pull from the guest’s recent LinkedIn posts. That’s when I realized the skills were doing exactly what they were told to do. Nothing more and nothing less. What was missing? Well, I only had part of the recipe — nothing was telling the Skills what was true here. Three more components fixed it.

A Skill on its own is one of those prompt workflows I worked on last year.^[1] What’s the secret? Well, if you combine four components, most of the quirks you keep noticing stop being structural in nature. Here’s what an effective Claude stack looks like, and what each component does:

• Skills run the plays: packaged workflows you trigger with a slash command, like /podcast-prep, /send-invoice, or /podcast-production.

• CLAUDE.md sets the rules: the project’s rulebook, which Claude reads at the start of every session in that folder.

• Memory holds the truth: the personal preferences, corrections, and decisions that follow you across every project.

• MCPs connect the tools: live connectors into Gmail, Sheets, Calendar, WordPress, and the rest of your stack.

Each ingredient on its own is just an ingredient. Combined in the right kitchen, they compound into a working stack.

A Skill alone is a prompt workflow

Earlier this month, I made the marketer’s case for Claude Code.^[2] That post explained why marketers should run Claude as a project tool. This piece covers the four components that make it stick once you do.

A Skill is a packaged recipe Claude calls with a slash command. It’s an atomized set of instructions for whatever you want to do in marketing. The recipe lives in a folder, alongside any helper scripts or examples it needs.^[3] You type /podcast-prep, Claude loads the instructions in the folder, and the skill runs. Type /send-invoice and you get a different recipe. The skill is the “how” of a repeated job. The great thing about them is that they load when you call them, not in every session. Why do you care? It saves tokens and keeps the context window from getting too bloated.

In isolation, that’s all it is. A Skill is a recipe with no kitchen, no pantry, and no cook’s preferences. The recipe says, “build a guest prep doc in this format.” And as we know, every recipe has the ingredients and the steps you need to follow to create that delicious meal. It does not say which guest you’re prepping for, whether the questions should lean technical or strategic, or which generic openers I’ve already banned. If you run the same /podcast-prep skill for two different guests, you’ll get the same mistakes both times. Nothing provides context or tells the skill what’s true about this guest, this episode, or what I want to avoid.

That’s where CLAUDE.md comes in. CLAUDE.md is the project’s rulebook, and Claude reads it every time you start a session in that project’s folder. The Skill brings the workflow, and the CLAUDE.md tells the Skill what’s true in this room. Together, they produce a coworker. Without CLAUDE.md, you’ve got a contract writer who hasn’t read the brief, which contains the bans, link conventions, and project-specific rules.

In this project, my CLAUDE.md tells the /podcast-prep skill to pull guest questions from the guest’s recent LinkedIn posts and prior podcast appearances, not generic conference talking points. It tells the skill that em-dashes are banned in the prep doc, that guest titles must match what’s on LinkedIn (not what’s on the company website), and that every question list opens with something tied to the guest’s recent work. The same /podcast-prep skill ran for two different guests, producing two different sets of prep notes: same recipe, different room.

“A Skill without CLAUDE.md doesn’t know which client it’s working for.”

Which fixes one half of the brittleness. CLAUDE.md handles the project. The other half is what travels with you across projects.

A CLAUDE.md alone is a wiki nobody reads

CLAUDE.md is the project-scoped rulebook. Drop a file named CLAUDE.md at the root of a project, and Claude reads it every session you start in that folder. The file contains voice rules, naming conventions, and project-specific bans such as “no em-dashes” or “no Amazon book URLs.”

It’s also useless on its own. A 500-line rulebook is wallpaper if nothing reads it, and rules in a file you can’t trigger don’t bind anything. Without memory, the rules stop at the project’s edge. CLAUDE.md says “first person, sentence case headings.” It does not say I prefer “leadership” over “the board.” That preference is mine, and it follows me across every client folder I open.

“Without memory, every CLAUDE.md is the same lecture I have to repeat in every project.”

Memory does the inverse of CLAUDE.md. Memory is what Claude remembers about how you actually work in this project, across every session. It holds your voice preferences, the decisions you’ve made about how to phrase things, and the file naming conventions you’ve earned the hard way. Each project gets its own memory folder, which lives outside the repo in your home directory, and Claude reads it at the start of every session.

In this project, the CLAUDE.md handles the format rules. Memory adds the word-level preferences I’ve corrected too many times to trust myself to remember. Project rules and personal preferences live in different files for a reason. Together, they steer the voice every time I work in this folder.

Some teams write the same file as AGENTS.md instead.^[4] It’s the cross-tool convention stewarded by the Linux Foundation’s Agentic AI Foundation, read natively by Codex, Cursor, and 20-plus other tools. Anthropic’s tooling reads CLAUDE.md, and some projects keep both files.

Project rules are sorted. Personal preferences live in a different file. That file has its own way of disappointing you when it works on its own.

Memory alone is sticky notes taped to your monitor

Memory in Claude is a folder of markdown files that the system reads at the start of every conversation, regardless of project. Mine has more than fifty entries. Here are five of them, lightly anonymized:

Sheet 1BojY1g is canonical for the calendar. Don’t edit the local CSV.

I prefer “leadership” over “the board.” Applies in every project.

Pull quotes attribute to “David Sweenor, Founder/CEO, TinyTechGuides.”

Buffer’s createPost API does not ingest video from URLs. Manual upload only.

Don’t update episodes.md or guest-list.csv until after the episode is recorded. Guests cancel.

Five real entries. They’re project-agnostic. They follow me from this TTG repo into a Posit project into a Solidatus project, every time I open Claude. Memory is what Claude knows about me, regardless of what I’m working on. Of course, I also have a memory for each of my client folders and, in some cases, client projects.

It’s also sticky notes on the monitor. The notes are useful when I’m sitting at the desk reading them. They don’t open Notion or send the Buffer post. They certainly don’t check what’s next on the Sheet. Memory tells Claude what I’ve decided. Acting on those decisions still takes me reaching for the keyboard. It’s the kind of bugaboo that looks fixed every time you stare at it.

Memory holds the Sheet ID, but something else has to actually read the Sheet. Memory remembers I want video posts queued in Buffer, but something else has to post them. The memory entry is just text, Claude can’t act on it without a connector to the tool itself.

“Memory tells Claude what you decided. MCPs let Claude do something about it.”

Which inverts the question. What does an MCP look like as the only component? It is loud, connected, and not very useful.

An MCP alone is an API call you’re making by hand anyway

Last quarter, I asked Claude to track down a follow-up email I’d sent a prospect two weeks earlier. The Gmail MCP happily searched 200 messages and returned 47 of them. None were the thread I was chasing.

That’s an MCP doing exactly what an MCP does. An MCP, or Model Context Protocol server, is a connector that lets Claude operate tools like Gmail, Sheets, and Calendar.^[5] Anthropic introduced the spec in late 2024, and it’s now the standard interface for AI applications to talk to external systems. If you’ve ever wanted Claude to send a follow-up email instead of drafting one for you to copy-paste, MCPs are how.

On their own, MCPs are raw access. The Gmail MCP can search your inbox. The Sheets MCP can read any range. Neither one knows which thread is the prospect call you’ve been chasing or which tab is the canonical source of truth. Raw access without instructions is “search 200 emails for X” on repeat, which is mostly the API call you’d be making by hand. You’ve just stuck Claude in the loop.

That’s where Skills come back in. A Skill is a recipe that turns “search 200 emails for X” into “find the threads with my recent prospect contacts and draft three follow-ups in my voice.” The Skill carries the workflow. The MCP carries the tool access. Skills plus MCPs is the difference between an API call and a finished workflow.

“An MCP without a Skill is a kitchen without a chef.”

My /podcast-production skill bundles ten clip drafts and queues them in Buffer in about ninety seconds. Each draft has the correct ATTACH header, so I know which MP4 to upload manually. The skill knows the format from CLAUDE.md, the Buffer account from memory, and the API call from the MCP. Pull any component, and the workflow stops being a workflow.

What the four components buy you

By the time you’ve stacked all four, you’ve built an operating system. Skills hand work to CLAUDE.md, which gives memory the rules. Memory feeds MCPs, which serve the next Skill. The output of one component becomes the input to the next in every session.

Consider the following questions:

1. Are your Skills reading the project rulebook, or are they running on default voice?

2. Does your CLAUDE.md exist in every project you switch between, or just the one you set up first?

3. Have you corrected the same preference more than twice this month? That’s a memory miss.

4. Are you still hand-copying data from Gmail, Sheets, or your CRM into chat?

Gartner put a sharper point on it at the D&A Summit in Orlando this March. Expecting AI to compensate for delayed upgrades, siloed teams, and missing context is “wishful thinking.”^[6] Gartner prescribes “a well-designed context layer” instead. The four components in this article are that context. The Skill is the tool that runs on top. The compounding makes them a marketing moat.^[7] Tools change, and components compound.

The next time your Claude setup feels like it’s not working quite right, look down. Whatever Skill you wrote is doing its job. The job is just bigger than one component.

More on the four-component stack every other Tuesday (or when I get around to writing about it) at tinytechguides.com or insights.tinytechguides.com. Skills and CLAUDE.md, memory and MCPs, and what breaks when any one of them goes missing. Subscribe if you want the next one in your inbox.

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Frequently asked questions

What is a Claude marketing OS?

A Claude marketing OS — sometimes called a Claude stack — is the four interlocking components that make Claude reliably useful for marketing work: Skills (slash-command recipes), CLAUDE.md (project rulebooks), memory (personal preferences across projects), and MCPs (connectors to tools like Gmail and Sheets). When the OS feels “quirky,” one of the four is missing or misaligned. Stacked together, they compound. The output of one component becomes the input for the next.

What is a Claude stack?

A Claude stack is the four interlocking components that make Claude reliably useful for repeated work: Skills (slash-command recipes), CLAUDE.md (project rulebooks), memory (personal preferences across projects), and MCPs (connectors to tools like Gmail and Sheets). Each component alone is limited. Stacked, they compound. Skills hand work to CLAUDE.md, CLAUDE.md gives memory the rules, memory feeds MCPs, and MCPs serve the next Skill. The output of one component becomes the input for the next.

What’s the difference between a Skill and a CLAUDE.md?

A Skill is a packaged recipe Claude calls with a slash command, like /podcast-prep or /send-invoice. The Skill carries the workflow steps. A CLAUDE.md is the project’s rulebook, located at the root of the project folder, and Claude reads it every session in that project. The CLAUDE.md file tells the Skill what’s true in this project, such as voice rules, naming conventions, and project-specific bans. Skills handle the “how.” CLAUDE.md handles the “what’s the context here.” Without CLAUDE.md, the Skill runs on the default voice.

What is an MCP in Claude?

An MCP, or Model Context Protocol server, is a connector that lets Claude operate the tools you already use, like Gmail, Sheets, Calendar, Notion, and Buffer. Anthropic introduced the spec in late 2024, and it’s now the standard interface for AI applications to talk to external systems. On its own, an MCP is raw access, like “search 200 emails for X” without context for which X matters. When paired with a Skill, the MCP becomes a finished workflow.

Why won’t a better Skill alone fix my Claude workflow?

Skills carry instructions for repeated tasks, but they don’t include project context, personal preferences, or access to tools on their own. A Skill running without CLAUDE.md doesn’t know which client it’s working for. Without memory, it forgets your preferences from session to session. The same Skill without MCPs can’t act on your tools at all. The Skill executes whatever you tell it. If nothing else in the stack tells it what’s true, the executions stay shallow. Sharper Skills don’t fix missing components.

Where should I start auditing my Claude stack?

Start with the four-question audit. Are your Skills reading the project rulebook, or are they running on default voice? Does your CLAUDE.md exist in every project you switch between, or just the one you set up first? Have you corrected the same preference more than twice this month? That’s a memory miss. Are you still hand-copying data from Gmail, Sheets, or your CRM into chat? Each “no” maps to a missing component.

Is AGENTS.md the same as CLAUDE.md?

Same idea, different filenames. AGENTS.md is the cross-tool convention stewarded by the Linux Foundation’s Agentic AI Foundation, read natively by Codex, Cursor, Gemini CLI, and 20-plus other tools. CLAUDE.md is Anthropic’s tooling convention. Both serve as project rulebooks that the agent reads at the start of each session. Some teams keep both files for cross-tool portability. The function is a project-scoped context layer, which matters more than the filename.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Sweenor, David. “PMM’s Prompt Playbook - Prompt Inventory.” insights.tinytechguides.com, February 21, 2025. https://insights.tinytechguides.com/p/pmms-prompt-playbook-prompt-inventory

^[2]Sweenor, David. “The marketer’s case for Claude Code.” TinyTechGuides, May 1, 2026. https://tinytechguides.com/blog/the-marketers-case-for-claude-code/

^[3]Anthropic. “Agent Skills Overview.” Claude API Docs. Accessed May 2026. https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview

^[4]Agentic AI Foundation. “AGENTS.md.” Linux Foundation, 2026.

https://agents.md/

^[5]Anthropic. “Introducing the Model Context Protocol.” November 25, 2024. https://www.anthropic.com/news/model-context-protocol

^[6]Gartner. “Gartner Identifies Three Pillars for Deriving Value from AI.” Press release, March 9, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-03-09-gartner-identifies-three-pillars-for-deriving-value-from-ai

^[7]Sweenor, David. “Marketing moats: what of that?” TinyTechGuides, May 8, 2026. https://tinytechguides.com/blog/marketing-moat-2026/

Everyone’s talking about moats

Well, it’s happened. The AI world is suddenly abuzz and enamored with moats, but these are not the stone walls and fetid water that surrounded medieval castles. These are metaphorical moats that provide a systemic competitive advantage. It seems like half of LinkedIn has rediscovered Hamilton Helmer this quarter. Software Equity Group, an M&A advisory firm that has tracked the SaaS sector for thirty years, reports that 85% of M&A buyers now name AI-driven commoditization as the number-one risk to SaaS valuations.^[1] The high-priced consultants got the memo, and brand is now the new moat. No wait, trust is the moat. Or perhaps, data flywheels are the moat? Every 2026-predictions post has a take, and most of those takes have some truths.

Dave Kellogg is right about trust. “Only trust will get people to open your emails,” he wrote in his 2026 predictions, “only trust will allow them to believe the reviews and testimonials about your product.”^[2] CTI Digital is right about brand in a sea of AI-generated sameness.^[3] Steven Cen is right about data flywheels.^[4] The consensus has converged for a reason.

Most of that writing is shaped by capital-market concerns. What investors want to know is what defends a SaaS company’s valuation when they get nervous about the impending AI bubble popping. That is a useful question for boards, founders preparing to sell, and PE shops doing diligence. For CMOs and marketing leaders trying to plan the next quarter, it’s not all that useful.

If you run a marketing function in 2026, “build a data flywheel” and “invest in brand” are not action items for next quarter. They’re often a multi-year investment and require diligence, a concerted effort, and likely, a considerable investment. These are real moats. You can start building them this Tuesday, but sadly, they will not be completed within the next cycle.

A related question that marketing leaders keep asking is: what can a marketing team build in the next quarter that is hard for a competitor to copy? Flywheels, brand, and trust moats in 2026 are outputs. This post is about the input.

Helmer’s forgotten power

Hamilton Helmer published 7 Powers in 2016. It is the strategy book Andreessen Horowitz hands to founders, and the closest thing the post-Porter generation has to a canonical framework.^[5] The book lists seven sources of durable competitive advantage: Scale Economies, Network Effects, Counter-Positioning, Switching Costs, Branding, Cornered Resource, and Process Power.

“I spent eleven years on a fab floor watching Process Power get built one wafer at a time. The machines to do it in marketing landed in 2024.”

— David Sweenor, Founder/CEO, TinyTechGuides

Six of the seven powers belong to the company and the CMO cannot act on them. Scale economies, switching costs, and network effects are not marketing decisions. You do not get to choose your scale curve or your churn dynamics from a marketing seat.

Process Power is the exception. Helmer defines it as “embedded company organization and activity sets which enable lower costs and/or superior product, and which can be matched only by an extended commitment.”^[6] His example is the Toyota Production System (TPS).

In the semiconductor world, we lived by a specific mantra: If the yield drops, the process failed you. We didn’t blame the operator; we fixed the system. Is the line losing wafers to defects? You use data to find the cause, codify the fix into the standard operating procedures, and ensure the next shift doesn’t rediscover the same failure.

In 2026, the marketing function will finally have the instruments to run the same playbook. It’s a mental shift from “campaign thinking” to “platform thinking.”

The operator stack is tool-neutral

A common critique of building an “AI moat” is platform risk. If you build your business on a single model, you’re building a house of cards. But the Operator Stack isn’t about the tool; it’s about the schema of your business logic. Whether you run on Claude, Gemini, or a local open-source model, the moat is the codified practice sitting in your markdown files.

The stack consists of four interlocking layers that behave like a production line:

• Skills: are the named, reusable workflows your team calls by command. A Skill bundles the quality bar, so the output is consistent, regardless of who (or what) is running the prompt.

• Rules (CLAUDE.md): are the standard work documents, distinct from voice guidelines or a brand brief. They capture what gets done every time and what never gets done. When a piece of content misses the mark, you don’t just edit the text. You update the Rules so the failure never repeats. The process failed you, so you fix the process.

• Memory: is the learned-context layer. It’s where your authenticity and unique data live. It turns your napkin files and messy first-party observations into a compounding asset.

• MCPs (Model Context Protocols): are the connective tissue. They remove the “hand-off tax” between the AI and your CRM, content sheet, or inbox. This is where you eliminate friction.

The human as system pilot

The key with all of this is to augment human ingenuity and capacity. You move from being a “Writer” to a System Pilot. Your job is to keep the stack fresh. At TinyTechGuides, I use a /reflect skill to audit the last ten sessions and identify where the rules are drifting or where the memory needs a “napkin file” update from the latest sales call.

A “Writer” is a commodity in 2026. A System Pilot is a high-value strategist who manages a compounding asset (the stack) to produce 10x the output at 10x the quality.

The human provides the “yield” by challenging the AI, conducting research, and feeding the system new, authentic inputs. The stack carries the load, but the pilot determines the destination.

“We do not have a content team. We have a content stack. The same person produces four times the output at the same quality bar, because the stack carries the load—the voice, the data, the authenticity—that used to live in heads.”

— David Sweenor, Founder/CEO, TinyTechGuides

Why this compounds faster than brand

Brand compounds. Trust compounds. Data flywheels compound. The operator-depth camp does not disagree with any of that. The argument is about cycle time.

“Brand compounds in five years. Operator depth compounds much more quickly. The math is not subtle.”

— David Sweenor, Founder/CEO, TinyTechGuides

Brand compounds over a five-year cycle, often with an eight-figure paid spend behind it. Most marketing functions are not catching incumbents on brand. The CMO who needs differentiation in 2026 is not waiting until 2031.

Trust compounds more slowly than brand. A B2B SaaS company earns trust through consistent shipping, customer outcomes that hold up under scrutiny, and reviews from named buyers. That work pays back, but it pays back over a five-to-ten-year arc. That is the arc Kellogg is talking about when he says only trust will get people to open your emails. It is right, and it is slow.

Data flywheels compound, once you have the data. Most teams do not. The flywheel works once you already have customer volume and the feedback loop in place. For an early-stage marketing function, “build a data flywheel” is closer to “build a customer base” than to “execute next week.”

Operator depth compounds on a ninety-day to six-month cycle. The CLAUDE.md you write this weekend starts paying back next Tuesday. The third Skill your team converts from a one-off prompt is sharper than the first, and the fifth is sharper than the third. The memory file accumulates, and the MCPs harden against the systems your team runs every day. By month six, the team is moving at a tempo competitors cannot match without doing the same work, which most of them are not yet doing.

This is why operator depth is the moat available to teams without a ten-year head start. The other moats favor incumbents. Operator depth is the input that makes the consensus moats reachable.

What CMOs should do this quarter

A CMO can start building operator depth in ninety days by running four moves in sequence. None require new headcount, new vendors, or a budget conversation with finance. This is the tactical version of the strategic posture.

“You do not need ten years. You need ninety days. The CLAUDE.md you write this weekend is the standard work document for everything your team ships next quarter.”

— David Sweenor, Founder/CEO, TinyTechGuides

Here are some practical steps you can take today:

Inventory the recurring work: Walk through what your team did last quarter. Anything the team executed more than three times is a Skill candidate. Every product-launch announcement, every analyst-relations note, every webinar promo qualifies. Pick the three highest-frequency workflows and write them as Skills first. The math is mechanical. If a workflow runs ten times a quarter and a Skill saves an hour each run, the Skill pays for itself the second time it runs.

Write a real CLAUDE.md: Not voice guidelines or operating rules. What every piece of content does every time, what no piece of content ever does, and which competitors are off-limits to cite. Treat it as the standard work document for the marketing line. The first version will be wrong in places. By the third pass, it starts saving you time.

Make memory a deliberate practice: Capture editorial preferences, brand decisions, and customer language at the moment they are made, not in a quarterly retro. The principle is the same one IBM ran on the fab line. The reorg-resistant version of institutional knowledge is the one that lives in a file, not in the head of the person who left.

Connect the systems your team runs: The moat is not in the AI tool. The moat is in how deeply the AI tool reaches into the sheet and the inbox, the CRM, and the analytics layer. An AI tool that cannot read your content calendar is a chatbot. The same tool, hooked into the calendar with MCPs and pushing drafts straight into the social queue, becomes a marketing operator.

Ninety days is enough to ship the first round of all four. The second round is when the compounding starts. If you want help mapping the operator stack for a B2B marketing function, that is the work TinyTechGuides runs with marketing teams every week.

The moat eventually dries up

Brand, trust, and data flywheels are real moats. Operator depth is also a real moat. Brand and trust take years and millions. Operator depth takes a quarter and curated hours.

The marketing functions building operator depth in 2026 are producing differentiated work faster than the brand-only camp can match. They are not winning forever. Eventually, the practice gets commoditized too. The toolkit gets standardized. The compounding flattens. Every moat in the history of marketing has had a ceiling, and operator depth will be no exception.

For the next year or so, it is the moat available to anyone willing to do the work. The window is open. The AI tools are sitting on the shelf. The discipline is the part nobody else is willing to copy yet.

If you want help mapping your function’s operator stack, that is the conversation. Until then, write the CLAUDE.md. The tools are already on the shelf.

Need help with your PMM strategy or compounding your knowledge? Schedule a consultation.

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Frequently asked questions

What is a marketing moat in 2026?

A marketing moat is a competitive advantage that compounds over time and cannot be quickly copied. The 2026 conversation has converged on brand, trust, and data flywheels. Each takes years and millions to build. Operator depth, the codified workflows and integrated tools that determine how fast a marketing team produces work, has joined the list because the instruments to build it landed in 2024. Hamilton Helmer would call it Process Power, applied to the marketing function for the first time.

How does operator depth differ from a tech stack?

A tech stack is the set of AI and martech tools your team has bought. Operator depth is the codified practice built on top of those tools. It includes named workflows, project rulebooks, and accumulated memory, plus MCP integrations into the systems where work happens. Two marketing teams can run identical stacks and produce different outputs because one has codified its operating practice and the other has not. The tools commoditize. The codification compounds.

Where do brand and trust fit if operator depth is the moat?

Brand, trust, and data flywheels are still real moats. They run on different time cycles. A brand takes five years and eight figures of paid spend to build. Trust requires a multi-year arc of consistent shipping and customer outcomes. Operator depth compounds in ninety days on a budget of curated hours. A CMO working on differentiation in 2026 cannot wait until 2031 to catch incumbents. Operator depth is the input that makes the consensus moats reachable inside a single planning cycle.

Can a small team really build operator depth in 90 days?

Yes, and a one-person marketing function often moves faster than a twelve-person team because the codification work is sequential. The first round is mechanical. Inventory the recurring workflows and write the project rulebook. Then capture decisions in memory as they happen and connect the AI tools to the systems your team runs every day. Ninety days is enough time for the first pass. The compounding starts in the second, when the workflows sharpen, and the memory file grows past anyone’s recall.

How does this connect to Hamilton Helmer’s 7 Powers?

Helmer’s 7 Powers (2016) lists seven sources of durable competitive advantage. Six are company-level decisions that a CMO cannot act on from a marketing seat. Process Power is the seventh, and the one that a marketing function can build on its own. Helmer’s canonical example is the Toyota Production System. Operator depth is the same logic applied to a marketing line. Skills and project rulebooks codify the standard work, and memory plus MCPs keep the codification durable across reorgs and integrated with the systems the team runs every day.

What’s the first thing to build?

Start with the project rulebook, the file that holds your team’s operating rules in one place. CLAUDE.md or your tool’s equivalent system-prompt file takes a weekend to write and pays back the next time you ship a piece of work. Capture what your team does every time, what it never does, and the brand voice every piece carries. Add the competitors off-limits to cite. Skills, memory, and MCPs come after. Without the rulebook to anchor them, those three layers drift away from the brand.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Software Equity Group. “2026 State of SaaS M&A: Buyers’ Perspectives.” Software Equity Group, 2026. https://softwareequity.com/research/saas-ma-buyers-perspectives.

^[2]Kellogg, Dave. “Kellblog Predictions for 2026.” Kellblog, January 22, 2026. https://kellblog.com/2026/01/22/kellblog-predictions-for-2026/.

^[3]CTI Digital. “2026 Marketing Trends: Brand as the Deepest Moat.” CTI Digital, 2026. https://www.ctidigital.com/insights/2026-trends-brand-as-the-deepest-moat/.

^[4]Cen, Steven. “AI Killed the Feature Moat. Here’s What Actually Defends Your SaaS Company in 2026.” Medium, 2026. https://medium.com/@cenrunzhe/ai-killed-the-feature-moat-heres-what-actually-defends-your-saas-company-in-2026-9a5d3d20973b.

^[5]Helmer, Hamilton. 7 Powers: The Foundations of Business Strategy. Deep Strategy LLC, 2016. https://www.7powers.com/.

^[6]Helmer, Hamilton. 7 Powers: The Foundations of Business Strategy. Deep Strategy LLC, 2016. https://www.7powers.com/.

Skip the Claude mode debate

Most marketing teams I’ve worked with this past year are running similar setups. ChatGPT or Gemini lives in a permanently open browser tab, and the team’s saved prompts live in a Slack channel or Google doc that they copy and paste from. That’s a great way to get started, but as usage matures, the team needs more coordination and better tooling. Teams have seen all of the hoopla around Claude Code, and many of my clients are moving in that direction. Inevitably, they always end up asking the same question. How do I get started with Claude and which mode (Chat, Cowork, or Code) should I be using?

Take a gander around Substack and you’ll see an endless debate about which mode to use when. Chat for quick questions, Cowork to help connect your files and tools like Gmail, and Code for the technical bits. My recommendation is the same one I follow myself. Use only Claude Code. That’s it.

Remember the movie The Matrix? This is the same red pill / blue pill choice that Neo had. The browser tab is the blue pill. It’s easy and familiar, every conversation is a fresh start, and nothing compounds. Claude Code is the red pill. The editor is unfamiliar for a day. After that, you can’t unsee what’s on the other side and there’s no turning back.

The install takes 15 minutes, the vocabulary another 10. This post covers both, plus the case for skipping the debate.

A tool is not a stack

Installing Claude doesn’t change the AI itself. The model in your browser tab and the model in Claude Code are the same Anthropic model. But, the wrapping and context around the model differ. The additional context you build inside Claude Code turns the AI from a tool into a full-fledged marketing operating system. You can use it on your own, or, as your team scales, you can share context across your entire marketing or GTM function.

Browser-tab AI gives you the model and a chat window. That’s it. Memory exists, but it’s behind a separate settings screen, and you update it manually. The memory itself may leak across contexts you didn’t ask it to span. When you advise multiple clients or work on anything sensitive like product roadmaps, acquisitions, and launch plans, that can be problematic to say the least. The conversation you had about your upcoming product launch is one ambient prompt away from showing up in your conversation or content that’s being pushed to public channels before you’re ready. Consultants don’t want info from Client A leaking into Client B. You have better control over this in Claude Code.

Chat-only setups don’t share very well. Your colleague can’t inherit the prompts and context you’ve built up. Every team conversation is like a Saturday Night Live (SNL) cold open. Claude Code gives you the model plus four pieces that surround it that are transformative..

1. Memory you can scope to a project, so what Claude knows about Client A stays with Client A.

2. Skills you write once and reuse across the team with a slash command.

3. CLAUDE.md is a markdown file the team can read and edit, so the brand voice and deliverable formats are the same whether you’re drafting or your colleague is.

4. MCPs wire Claude into the tools you already use, like Gmail, Sheets, and Calendar. You don’t switch tabs or copy data into a chat. Claude reaches into the tools where the data lives.

Skills, CLAUDE.md, and MCPs all live in Code mode. Memory follows you across modes, and Code lets you scope it per project. That’s not coincidence. It’s why the rest of this post leans toward Cursor.

The 15-minute install

Four installs, about 15 minutes total. You can do this between meetings. I’ve run this install on my Mac and helped clients on Windows machines, and it doesn’t take long. To get started, follow these steps:

1. Start with a free GitHub account (2 minutes). This is where your team’s repo lives. Have the repo owner add you and accept the invite when it lands in your inbox.

2. Next, download Cursor and sign in with your work account (5 minutes). Cursor is the workspace where Claude Code runs. Defaults are fine and there is no need to buy a subscription.

3. Then install the Claude desktop app (3 minutes). You’ll mostly live in Code, but the desktop app is where you manage your account, and it pairs with Claude Code on the same login.

4. Finally, install Claude Code itself (5 minutes). Open Cursor’s terminal (View → Terminal) and paste the install command for your OS. On macOS or Linux, that’s curl -fsSL https://claude.ai/install.sh | bash. On Windows PowerShell, it’s irm https://claude.ai/install.ps1 | iex. Type claude and sign in when prompted. Claude Code requires a paid Anthropic plan (Pro, Max, Team, or Enterprise).

Here’s the checklist:

• Open Cursor and sign in

• Open the Claude desktop app and sign in

• Run claude in Cursor’s terminal without errors

• See your team’s repo on your GitHub account

When you get stuck, ask Claude. It can walk you through any install error, terminal command, or path issue you hit.

The three modes (and the one you need)

“Chat answers. Cowork connects to your tools. Code does all of that and more.”

— David Sweenor, Founder/CEO, TinyTechGuides

You’ll see three modes in the docs. Here’s what each one does, then what to do with that information.

• Chat: Back-and-forth conversation in a window. Best for quick questions, drafts, and brainstorming.

• Cowork: Claude plans and executes a task on your files, apps, and browser. Best for multi-step work that doesn’t live in a repo.

• Code: Terminal-based, runs inside a repo. Best for shared team docs, branded decks, and anything you want versioned.

Each mode has its own use case in the docs and its own corner of the marketing AI internet defending it. Last week, I saw a debate about whether spreadsheet research belongs in Cowork or Code. The week before, someone argued that brainstorming should never happen anywhere except Chat.

Each of the modes above is technically true, but none of them gets you to a marketing OS. Code does.

Code is where Skills, CLAUDE.md, and MCPs live. MCP stands for Model Context Protocol, the way Claude connects to other systems like Gmail, Asana, Canva, and Calendar. Code is where work compounds across sessions, where memory gets project-scoped, and where the team can see and edit what you made (via GitHub).

The most underrated benefit shows up when you need something none of those four pieces provide. An action-item tracker that pulls from your meeting notes. A status-report rollup across all your clients. Or a one-off Python script to clean up your Downloads folder. Code writes that on the fly. Browser-tab AI can’t write code that touches your files. Code can.

Teams matter too. When you ship work in Code to GitHub, your colleague clones the repo and inherits everything you’ve built. The Skills you wrote. The CLAUDE.md that defines the brand voice. The project-scoped memory rules. Browser-tab AI doesn’t ship. Your work stays on your account, where no one else can use it.

The Cursor interface is unfamiliar for the first day. The first 30 seconds are the worst. There’s a sidebar, a file tree, and a terminal pane. Push through it. After that, you’ve crossed the line, and you don’t go back, ever.

The four components that make Claude Code so powerful

Four components configure Claude. Three define how Claude works. The fourth wires Claude into the tools where your work already lives.

Memory is about you. Your role, your voice preferences, and how you like content formatted. Personal and portable across every project you work in. You set it once, and Claude carries it from session to session. After three months of corrections, the memory you’ve built is what makes Claude sound like you instead of a smart intern.

Skills are about the task. A Skill is a reusable recipe (instructions plus examples) that you call with a slash command. /write-blog. /generate-deck. /prep-podcast. Each one bundles the steps Claude should take for that task and the format of the output. For anyone already writing prompt workflows, Skills are how those workflows become team-shareable assets in your repo. Write a Skill once, and it works the same way every time you call it.

CLAUDE.md is about the project. It’s a markdown file in the project’s repo that Claude reads every time it works in that scope. It’s where you write down what’s true for this project. Brand voice. Audience. Deliverable formats. Drop one in your TTG repo, drop another in your client repo, and Claude switches voices without you reminding it.

MCPs wire Claude into the tools where your work already lives. Where Memory, Skills, and CLAUDE.md tell Claude how to work, MCPs tell Claude where to reach. Each MCP connects Claude to a specific tool, like Gmail, Sheets, Calendar, or Asana. You don’t have to copy data into a chat. Install one and Claude reaches into that tool. Install several and Claude works across your whole toolchain.

“Memory is about you. Skills are about the task. CLAUDE.md is about the project, and MCPs connect your apps.”

— David Sweenor, Founder/CEO, TinyTechGuides

A growing set of community patterns extends the same idea. A popular one is napkin.md, a per-repo runbook that Claude reads at session start and curates as you work. It catches mistakes once and stops repeating them. You commit it or you don’t, your call.

Your first move

You finished the install. Now what?

Open Cursor’s terminal, run claude, and have a five-minute conversation. Tell Claude who you are. Tell it what you do. Tell it the three things you reach for most often in a week. Save what you tell it as memory.

That’s the first move. Anything more is week-two work. Your first Skill conversion, your first CLAUDE.md, and your first MCP can wait. None of that matters in week one. Build the habit of opening Cursor, typing claude, and treating it like a colleague who started Monday.

Take the red pill

Fifteen minutes is the easy part. The IDE filters out most marketers. The marketing OS rewards the ones who push through.

Most marketers won’t cross the gate, which is why their AI output never compounds. You’re reading this, which means you’re closer than you think. Cross the gate. Take the red pill.

This post is the entry point for the Claude for Marketing Operators series on TinyTechGuides. The next eight posts walk through each layer of your marketing OS. The four-layer mental model. CLAUDE.md teardowns from real client projects. The MCPs that pull their weight, and the ones that don’t. What six months of memory looks like across multiple clients. How a solo consultant runs four engagements on one brain. Why your marketing OS becomes the moat your competitors can’t copy.

If you want each one as it drops, subscribe to the Marketing section on Substack.

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Frequently asked questions

What is Claude Code?

Claude Code is the terminal-based mode of Anthropic’s Claude AI assistant, built for work that lives in a code repository. For marketers, it runs inside an editor like Cursor and lets you maintain a project rulebook (CLAUDE.md), reusable Skills, scoped memory, and connectors (MCPs) to tools like Gmail and Sheets. It’s also the only mode that can write Python on the fly to handle tasks none of those four pieces cover, like building an action-item tracker or a status-report rollup across clients.

How does Claude Code differ from Chat and Cowork?

Chat is back-and-forth conversation. Cowork lets Claude plan and execute multi-step tasks across your files, apps, and browser. Code runs in your terminal inside a code repository, so the work compounds across sessions through Skills, CLAUDE.md, scoped memory, and MCPs. Chat and Cowork are useful for narrow tasks. Code is where reusable assets live and where teams can share what one person built. For marketing operators using Claude in production, the recommendation is to skip the mode debate and run everything in Code.

Do I need to be a developer to use Claude Code?

No. Claude Code runs in a terminal, but you don’t need to write code to use it. You install it once, then talk to Claude in plain English. The terminal interface is unfamiliar for the first day, but most marketing tasks (drafting blog posts, building decks, summarizing client meetings, packaging prompts as reusable Skills) work the same way they do in a chat window. The benefit is that everything you do compounds across sessions and can be shared with your team through a repo.

Does Claude Code require a paid Anthropic plan?

Yes. Claude Code requires a Pro, Max, Team, or Enterprise plan from Anthropic. The free Claude.ai plan does not include Claude Code access. The desktop app, browser-based Claude.ai, Cursor, and GitHub are all free — only the Claude Code CLI itself needs a paid plan. You can install all four pieces (GitHub, Cursor, Claude desktop app, Claude Code) before deciding on a plan, since the install completes before authentication.

What’s the difference between memory and a CLAUDE.md file?

Memory is personal and portable across every project you work in: your role, voice preferences, and how you like content formatted. CLAUDE.md is project-specific. It’s a markdown file in a project’s repo that Claude reads every time it works in that scope, capturing brand voice, audience, deliverable formats, and anything else that’s true for that project. Memory follows you across modes; CLAUDE.md stays with the project. A solo consultant runs one memory and many CLAUDE.md files, one per client.

Where should I start if I want to use Claude for marketing work?

Install the four pieces (GitHub, Cursor, Claude desktop app, Claude Code) in about 15 minutes. Then open Cursor’s terminal, run claude, and have a five-minute conversation. Tell Claude who you are, what you do, and the three things you reach for most often in a week. Save what you tell it as memory. That’s the entire first move. Skill conversion, CLAUDE.md drafting, and MCP setup are all week-two work. Week one is opening the terminal and starting to use it.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Consensus Path Planning and Deal Acceleration

Created by insights.tinytechguides.com

What this workflow does

I’ve watched deals die where every single stakeholder privately supported the purchase. No one objected. No one said no. The deal just stalled because nobody created a mechanism to turn all that private support into a collective decision.

When a deal goes quiet, sales teams usually blame a lack of motivation. The champion went dark. The economic buyer stopped responding. The technical team wants more data. These look like engagement problems. They’re structural failures. The committee never reached a specific consensus milestone, and nobody noticed because the pipeline report tracks stages, not alignment.

This workflow maps the path from first engagement to signed deal. It identifies the sequence of stakeholder engagements, the milestones the committee must reach, the mechanisms that build collective commitment, and the recovery protocols for when deals stall. The output is a deal architecture document that gives sales a stage-by-stage engagement plan and gives marketing visibility into where their content supports committee alignment.

This workflow does not create messaging, map the committee, or surface hidden objections. That’s what the first three workflows in this series do.

It answers one question: what is the specific path from fragmented interest to collective commitment, and what do you do when that path breaks?

Workflow steps summary

Step 0: Define inputs

Step 1: Define the consensus milestones

Step 2: Map the stakeholder engagement sequence

Step 3: Identify consensus-building mechanisms

Step 4: Design the stall recovery playbook

Step 5: Build the deal architecture timeline

Step 6: Assemble the consensus path plan

This is the fourth and final workflow in the buying committee series. It builds on the outputs from the first three:

• Buying Committee Decision Mapping Workflow

• Hidden Objection and Risk Surface Analysis

• Committee-Aware Messaging and Content Mapping

The workflow was created by insights.tinytechguides.com and connects to these existing workflows:

• Prompt Workflow: Voice of Customer

• Prompt Workflow: Competitive Landscape Mapping

• QuickStart Battlecard for Competitive Sales Wins

• Strategic Battlecard Workflow for Competitive Wins

Step 0: Define inputs

Before running the workflow, gather these inputs:

• {account_type} = target account profile or segment

• {deal_examples} = recent won, lost, and stalled deals

• {sales_inputs} = call notes, objections, deal commentary

• {customer_inputs} = outputs from Voice of Customer workflow

• {competitive_context} = outputs from Competitive Landscape Mapping

• {committee_map} = output from Buying Committee Decision Mapping Workflow

• {risk_analysis} = output from Hidden Objection and Risk Surface Analysis Workflow

• {messaging_map} = output from Committee-Aware Messaging and Content Mapping Workflow

• {avg_sales_cycle} = average sales cycle length for this deal size

• {sales_process} = current sales stages or methodology (e.g., MEDDPICC, Sandler, custom stages)

Step 1: Define the consensus milestones

A signed deal is the final milestone, but it is the result of many smaller consensus events that happen inside the buying organization. Before a committee can approve a purchase, they must collectively agree on a series of intermediate decisions. That the problem is worth solving. That the category of solution is right. That this vendor is the best fit. That the timing is right. That the terms are acceptable. Missing any of these intermediate consensus points creates a stall, and most sales teams cannot tell you which milestone their deal has actually reached versus which one they have assumed.

Prompt:

Role

You are a senior B2B sales strategist who designs deal architectures for complex enterprise sales involving multi-stakeholder buying committees.

Context

A signed deal requires the committee to reach consensus on five intermediate decisions: problem significance, solution category, vendor preference, timing, and terms. Missing any of these creates a stall that shows up as a “stuck” deal in the pipeline.

Task

Using {committee_map}, {risk_analysis}, {sales_inputs}, and {deal_examples}, define the internal consensus milestones for a purchase in {account_type}.

Format

For each of the five milestones (problem consensus, category consensus, vendor consensus, timing consensus, terms consensus):

• Description of what agreement looks like at this stage

• Key stakeholders who must be aligned

• Evidence they need to reach alignment

• The gatekeeper for this milestone

• The most common reason this milestone stalls

• The event or evidence that unlocks it

Deliver as a table. After the table, provide a narrative summary (3-5 sentences) describing the typical sequence and where the buying process most often breaks down.

Tone

Strategic and grounded in real-world sales dynamics. Write as someone who knows the difference between a milestone that has been genuinely reached and one that has been assumed.

Your pipeline isn’t stuck. It just skipped a milestone nobody noticed.

Step 2: Map the stakeholder engagement sequence

The order in which you engage stakeholders matters as much as the message. Engaging the economic buyer too early, before the technical team has validated feasibility, undermines credibility. Engaging procurement too late, after expectations are set without their input, creates adversarial dynamics. The right sequence builds momentum by creating a chain of internal endorsements where each stakeholder’s support makes the next engagement easier.

Prompt:

Role

You are a senior enterprise sales architect who designs multi-threaded engagement strategies for complex B2B deals with large buying committees.

Context

Buying committees do not engage in a linear sequence. Some stakeholders can be engaged in parallel. Others must be sequential. The wrong order creates friction that compounds across the deal cycle.

Task

Using the consensus milestones from Step 1, {committee_map}, and {sales_inputs}, design the optimal stakeholder engagement sequence for {account_type}.

Format

1- Engagement timeline table with columns: Stakeholder, First Engagement Timing, Engaged By, Goal of First Engagement, Prerequisites

2- Multi-threading strategy describing which stakeholders can be engaged simultaneously, which must be sequential, and where the champion should make introductions versus where sales should request meetings directly

3- Escalation triggers table with columns: Stall Signal, Escalation Action, Who to Involve, Anti-Pattern to Avoid

4- A text-based engagement sequence showing the flow across deal stages

Tone

Tactical and specific. Build this for a sales team executing on real deals, not a training manual.

Step 3: Identify consensus-building mechanisms

Individual stakeholder buy-in does not automatically become committee consensus. A committee where every member privately supports the purchase can still fail to act because no one created the mechanism for converting private support into collective commitment. Consensus requires structure. Shared evaluation criteria, visible commitment signals, and events where alignment is tested and confirmed rather than assumed.

Prompt:

Role

You are a senior organizational psychologist and B2B sales strategist who specializes in the dynamics of group decision-making within enterprise buying committees.

Context

The gap between individual interest and collective commitment is where most deals die. Consensus requires shared frameworks, commitment events, and coalition management, not just good meetings with individual stakeholders.

Task

Using the outputs from Steps 1-2, {committee_map}, and {risk_analysis}, identify the mechanisms that build consensus within this buying committee for {account_type}.

Format

1- Shared evaluation framework with 5-8 weighted criteria the committee can use to evaluate together rather than in isolation

2- Commitment escalation events table with columns: Event, Purpose, Success Signal, Facilitation Notes

3- Coalition-building tactics as a numbered list, each with a 2-3 sentence description covering alliance strengthening, friction neutralization, swing vote conversion, and blocker isolation

4- Commitment artifacts table with columns: Artifact, Associated Milestone, Owner, Why It Matters

Tone

Practical and psychologically informed. Write as someone who understands both the organizational dynamics and the tactical moves that convert understanding into action.

Step 4: Design the stall recovery playbook

Every complex B2B deal stalls at some point. The question is not whether stalls happen but whether the team can diagnose the cause and execute a recovery before the deal slips to “no decision.” Most sales teams treat stalls as a motivation problem when they are actually a structural problem. A specific consensus milestone was not reached, or a specific stakeholder’s concern was not addressed, and no one noticed until the deal went quiet.

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Committee-Aware Messaging and Content Mapping

Created by prompts.tinytechguides.com

What this workflow does

Most B2B messaging frameworks build one core value proposition with three or four supporting pillars underneath. That structure works for demand generation. It breaks down the moment a deal enters committee evaluation.

A buying committee has five to ten stakeholders who evaluate the same purchase through different lenses. The CFO is filtering for cost predictability. The VP of Engineering is scanning for integration risk. The end user wants to know if this thing will make their life easier or harder. A message that resonates with the champion may be irrelevant to the blocker and threatening to the person who owns the budget.

This workflow builds a messaging architecture that maps specific messages, proof points, and content assets to each committee role at each stage of the buying process. It includes a champion enablement toolkit and an objection-to-message bridge that connects every anticipated concern to a pre-built response.

This workflow does not map the committee or surface hidden objections.

That’s what the first two workflows in this series do.

It answers one question:

“What should we say, to whom, at what stage, and through what channel to move the entire committee toward a decision?”

Workflow steps summary

Step 0: Define inputs

Step 1: Define messaging pillars by committee role

Step 2: Map message-to-stage alignment

Step 3: Identify content gaps and build asset briefs

Step 4: Build the champion enablement toolkit

Step 5: Create the objection-to-message bridge

Step 6: Produce the messaging and content map

This is the third workflow in the buying committee series. It builds on the outputs from the first two:

- Buying Committee Decision Mapping Workflow

- Hidden Objection and Risk Surface Analysis

The workflow was created by prompts.tinytechguides.com and connects to these existing workflows:

- Prompt Workflow: Voice of Customer

- Prompt Workflow: Competitive Landscape Mapping

- QuickStart Battlecard for Competitive Sales Wins

- Strategic Battlecard Workflow for Competitive Wins

Step 0: Define inputs

Before running the workflow, gather these inputs:

- {account_type} = target account profile or segment

- {deal_examples} = recent won, lost, and stalled deals

- {sales_inputs} = call notes, objections, deal commentary

- {customer_inputs} = outputs from Voice of Customer workflow

- {competitive_context} = outputs from Competitive Landscape Mapping

- {committee_map} = output from Buying Committee Decision Mapping Workflow

- {risk_analysis} = output from Hidden Objection and Risk Surface Analysis Workflow

- {current_messaging} = your current messaging framework, value propositions, or sales deck narrative

- {existing_content} = list of current content assets (case studies, white papers, blog posts, webinars, ROI tools)

Step 1: Define messaging pillars by committee role

Traditional messaging builds a single value proposition hierarchy. One core message, three supporting pillars, proof points underneath. That structure collapses when a committee of seven people reads it through seven different lenses.

Prompt:

# Role

You are a messaging architect building role-specific messaging for a complex B2B buying committee.

# Context

B2B buying committees contain stakeholders who evaluate the same purchase through different priorities. A value proposition that resonates with the champion may be irrelevant to the blocker.

# Task

Using {committee_map}, {risk_analysis}, {current_messaging}, and {sales_inputs}, define messaging pillars for each committee role in {account_type}.

# Format

For each role:

- Primary messaging pillar (the single most compelling message for this stakeholder)

- Supporting proof points (2-3 specific evidence types that make the message credible)

- Language register (how this stakeholder prefers to receive information)

- Message to avoid (the framing that would backfire with this stakeholder)

Then provide the overarching narrative thread that connects all role-specific messages into a coherent story the champion can use to build consensus.

# Tone

Strategic and precise. The same product needs to mean different things to different people without contradicting itself.

Your champion’s pitch fails the moment it reaches someone who wasn’t in the room

Step 2: Map message-to-stage alignment

Not every stakeholder is active at every stage. A message that works during early education feels repetitive during evaluation. A message designed for final approval feels premature during discovery. Timing the right message to the right person at the right moment is where most go-to-market motions fall apart.

Prompt:

# Role

You are a demand generation strategist designing multi-stakeholder engagement sequences for complex B2B deals.

# Context

Buying committees do not engage with vendors all at once. Different stakeholders enter at different stages with different information needs.

# Task

Using the messaging pillars from Step 1, map message delivery across these buying stages: Awareness, Education, Evaluation, Consensus Building, and Negotiation/Approval.

# Format

A matrix with stakeholder roles as rows and buying stages as columns. In each cell:

- Message summary

- Content format (case study, ROI calculator, technical brief, etc.)

- Delivery channel (website, email, sales conversation, champion shares internally)

- Who delivers it (marketing, sales, champion, executive sponsor)

Mark “Not active at this stage” where a stakeholder is not yet engaged.

After the matrix, identify the 2-3 most critical message-timing combinations where getting it right has the highest impact on deal progression.

# Tone

Operational and specific. Build this for a team that needs to create campaigns from it.

Step 3: Identify content gaps and build asset briefs

Most B2B content libraries are built around product features or thought leadership themes rather than buyer committee needs. This creates gaps where critical stakeholders at critical moments have no relevant content available, and surplus where content exists but doesn’t map to any specific buying decision.

Prompt:

# Role

You are a content strategist auditing a B2B content portfolio against buying committee needs.

# Context

Content should map to specific stakeholders at specific stages. Content that doesn’t serve a stakeholder-stage combination is either a gap or clutter.

# Task

Using the message-to-stage matrix from Step 2 and {existing_content}, conduct a content gap analysis.

# Format

1- Coverage matrix matching Step 2’s structure, with each cell marked as Covered, Partially Covered, or Gap

2- Prioritized list of content gaps ranked by impact (high-influence stakeholder at a deal-critical stage = higher priority)

3- Asset briefs for the top 5 gaps, each including: asset type, target stakeholder, buying stage, key message, required proof points, and estimated production effort

4- Content retirement candidates that don’t map to any stakeholder-stage combination

# Tone

Analytical and practical. Write for a content lead planning next quarter’s production calendar.

Because a case study nobody needs is worse than a gap you know about

Step 4: Build the champion enablement toolkit

The internal champion is the most important and most under-supported person in a B2B deal. They carry the burden of building consensus, but most vendors equip them with product decks and datasheets designed for vendor-to-buyer conversations. The champion needs materials for buyer-to-buyer conversations. Materials that sound like something a colleague would say, not something a marketing team wrote.

Prompt:

# Role

You are a sales enablement strategist arming internal champions with tools to build consensus inside buying committees.

# Context

Champions lose credibility when they forward vendor materials that read like marketing copy. They need internal-facing narratives, talking points, and business case structures that work in hallway conversations and internal emails.

# Task

Build a champion enablement toolkit using the messaging architecture from Steps 1-3 and the risk analysis from {risk_analysis}.

# Format

1- Internal pitch narrative (3-5 sentences the champion can say to peers about why this purchase matters now, in plain language)

2- Stakeholder-specific talking points table with columns: Target Stakeholder, Emphasize, Avoid, Proof Point to Reference

3- Internal business case outline with section names, required data points, and narrative arc

4- Objection preemption scripts for the 3 most likely committee objections (conversational, not rehearsed)

5- “What if we do nothing” argument (3-4 sentences about organizational cost of inaction, not product benefits)

# Tone

Conversational and human. Everything should sound like something a real person would say to a colleague.

The champion’s credibility dies the moment they start sounding like a vendor

Step 5: Create the objection-to-message bridge

Objection handling in most B2B organizations is ad hoc. Sales reps develop their own responses based on experience, and marketing creates content without knowing which specific objections it needs to address. The result is inconsistent messaging and missed opportunities to neutralize concerns before they escalate.

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Hidden Objection and Risk Surface Analysis

Created by prompts.tinytechguides.com

What this workflow does

Most sales teams prepare for the objections buyers raise in meetings. Price. Timeline. Feature gaps. Integration concerns. Those are the objections people feel comfortable voicing because they sound rational and defensible.

The objections that actually kill deals are the ones nobody says out loud.

A VP of IT who says “we need more time to evaluate” is really thinking “if this implementation fails, that’s my reputation on the line.” A CFO who pushes back on price is really wondering whether the board will question the spend. A line-of-business leader who keeps requesting more references is stalling because their IT peer hasn’t been consulted and could undermine the project later.

This workflow surfaces those hidden fears, maps the structural conditions that make “no decision” feel like the safest option, and produces a risk mitigation playbook your team can actually use.

This workflow does not create content or messaging.

That’s the next workflow in this series.

It answers one question:

“What is everyone on the committee afraid to say out loud, and what do you do about it?”

Workflow steps summary

• Step 0: Define inputs

• Step 1: Surface hidden objections by role

• Step 2: Map the “no decision” incentive structure

• Step 3: Identify organizational risk triggers

• Step 4: Build the risk mitigation playbook

• Step 5: Produce the risk surface analysis

This workflow builds on the Buying Committee Decision Mapping Workflow, which maps who is involved in the decision and where power sits. If you haven’t run that workflow yet, start there. The committee map it produces feeds directly into this one.

- Buying Committee Decision Mapping Workflow:

The workflow was created by prompts.tinytechguides.com and connects to these existing workflows:

- Prompt Workflow: Voice of Customer: https://prompts.tinytechguides.com/p/prompt-workflow-voice-of-customer

- Prompt Workflow: Competitive Landscape Mapping: https://prompts.tinytechguides.com/p/prompt-workflow-competitive-landscape

- QuickStart Battlecard for Competitive Sales Wins: https://prompts.tinytechguides.com/p/prompt-workflow-battle-card-development

- Strategic Battlecard Workflow for Competitive Wins: https://prompts.tinytechguides.com/p/strategic-battlecard-workflow-for

Step 0: Define inputs

Before running the workflow, gather these inputs:

- {account_type} = target account profile or segment

- {deal_examples} = recent won, lost, and stalled deals

- {sales_inputs} = call notes, objections, deal commentary

- {customer_inputs} = outputs from Voice of Customer workflow

- {competitive_context} = outputs from Competitive Landscape Mapping

- {committee_map} = output from Buying Committee Decision Mapping Workflow

- {win_loss_data} = deal post-mortems, win/loss analysis, notes on why deals stalled or were lost

Step 1: Surface hidden objections by role

Buyers voice proxy objections in meetings because the real concerns feel too personal or too political to say to a vendor. This prompt separates what buyers say from what they actually think.

Prompt:

# Role

You are a B2B sales psychologist analyzing the gap between what buyers say and what they actually fear.

# Context

Enterprise buying committees voice rational-sounding objections in vendor conversations. Price, timeline, features. These are proxies. The real concerns are career risk, political exposure, and the fear of championing a decision that could fail publicly.

# Task

Using {deal_examples}, {sales_inputs}, {win_loss_data}, and {committee_map}, identify the hidden objections for each role on the buying committee for {account_type}.

# Format

For each role:

- Surface objection (what they say)

- Hidden objection (what they actually think)

- Root fear (the career, political, or operational fear underneath)

- Behavioral signal (how this fear shows up in the buying process)

# Tone

Psychologically precise. Write as someone who knows the difference between what buyers say and what they mean.

No one loses their job for saying “we need more time to evaluate”

Step 2: Map the “no decision” incentive structure

Across industries, 40-60% of qualified B2B pipeline ends in no decision because committee decision-making creates asymmetric incentives. The personal cost of championing a purchase that fails is high, and the personal cost of doing nothing is almost zero. When nobody gets fired for maintaining the status quo, inaction becomes the rational default.

Prompt:

# Role

You are a behavioral economist analyzing why enterprise buying committees default to inaction.

# Context

“No decision” is the largest competitor in B2B sales. Committee decision-making creates asymmetric personal incentives that favor inaction over action, even when the business case is sound.

# Task

Using the hidden objections from Step 1, analyze the incentive structure for this buying committee.

# Format

For each role:

- Personal gain if the purchase succeeds

- Personal loss if the purchase fails

- Personal loss if no decision is made

- Asymmetry direction (favors action or favors inaction)

Then provide:

- A short analysis of the collective action problem (why the group defaults to inaction even when individuals might benefit)

- The status quo anchors (existing tools, processes, or workarounds that make “good enough” feel acceptable)

# Tone

Analytical and unsentimental. Incentives, not intentions.

Share this with someone wondering why their pipeline is “stuck”

Step 3: Identify organizational risk triggers

A buying committee’s willingness to act depends on timing and context as much as product value. Budget cycles, leadership changes, a failed competitor implementation, a regulatory deadline. These conditions either create urgency or amplify risk aversion.

Prompt:

# Role

You are a market strategist analyzing the organizational and market conditions that influence enterprise buying behavior.

# Context

A buying committee’s appetite for action depends on timing and context as much as product value. Conditions inside and outside the organization can accelerate or stall decisions.

# Task

Using {deal_examples}, {sales_inputs}, and {competitive_context}, identify the conditions that affect this buying committee’s willingness to act.

# Format

- Urgency accelerators (conditions that push the committee toward a decision, and which roles they affect most)

- Risk amplifiers (conditions that increase reluctance, and which roles they affect most)

- Timing windows (when in the fiscal year or organizational rhythm this committee is most and least likely to approve a purchase)

- Trigger events (3-5 specific events that would force this committee to act, even if the incentive structure otherwise favors inaction)

# Tone

Strategic and observational. Timing matters as much as product value.

Because timing a deal right is harder than building a business case

Step 4: Build the risk mitigation playbook

The goal is not to overcome objections through argument. Arguments trigger defensiveness. The goal is to change the conditions that make objections feel necessary in the first place. Each hidden concern has a structural root, and the most effective mitigation strategies address the structure rather than the symptom.

Read more

For every product marketer, personas and ICPs are foundational content that inform much of the content, sales plays, demand-gen programs, and enablement content. I’ve spent years building detailed personas, the kind with headshots, job titles, link-outs to LinkedIn profiles, goals, pain points, and objections neatly organized in a slide deck that looked great in a quarterly business review (QBR). At SAS, Dell, Alteryx, Alation, and now many of my clients, I’ve watched teams invest weeks refining these documents, convinced that if we could just describe the buyer precisely enough, the pipeline would follow.

Here’s what I’ve learned. Personas describe individuals. Groups make purchase decisions in B2B, and those groups are uncoordinated and messy.

The belief that buying decisions are driven by individuals acting on clearly defined needs is one of the most persistent myths in B2B marketing. Entire marketing motions and demand-gen campaigns are built around it. Teams create a hypothetical buyer’s goals, segment them into top of funnel (TOFU), middle of funnel (MOFU), and bottom of funnel (BOFU), map their pain points, catalog their objections, and then build campaigns aimed squarely at that one profile. The problem with personas is that they are radically incomplete. They capture a slice of the picture and treat it like the whole thing.

In real B2B environments, decisions are rarely made by a single person optimizing for a single outcome. They are made by groups of people with overlapping authority, misaligned incentives, and very different definitions of risk. What looks like a straightforward purchase from the outside is, internally, a negotiation about tradeoffs, accountability, and exposure. This is why so many deals that appear healthy simply stall.

Why deals stall without a “no”

Most experienced marketers and sales leaders have encountered some version of the same pattern. The buyer is engaged, and the solution is aligned. The conversations are going well and are constructive. There is no explicit objection, no formal rejection, and no stated competitor win. And yet the deal fails to close. Isn’t “No competition” the most frequent “loss reason” in Salesforce? So why didn’t the deal close?

I watched this happen in many deals over my career. Every signal said “yes.” The champion was enthusiastic, the technical team had validated the solution, and leadership had even nodded along in the executive briefing. Then nothing happened. Weeks turned into months, and the deal just sat there, ghosted.

When deals stall like this, the post-mortem often defaults to surface explanations like budget cycles, shifting priorities, and internal delays. Those factors may be present, but they usually obscure something more fundamental. In Modern B2B Marketing, I cited Forrester’s B2B Buying Survey, which found that 60% of B2B purchases involve groups of four or more people, with committees of seven to ten members engaging in an average of 27 interactions across cycles that often exceed four months. With that many people, touchpoints, and months in play, the committee simply could not reach internal consensus without someone taking on unacceptable risk.

In these situations, no one wants to be the person who champions a decision that peers, superiors, or adjacent functions could later question. The safest option becomes inaction. The deal doesn’t die; it simply never resolves. What looks like indecision from the outside is usually self-preservation on the inside.

The limits of persona-based thinking

Traditional persona frameworks struggle to account for this reality because they assume coherence where none exists. They describe buyers as if priorities are stable, aligned, and internally consistent. In practice, those priorities are negotiated across roles with different incentives over a span of time that can last months or even years.

A CFO evaluating a purchase is often optimizing for predictability and downside protection. A functional leader may be optimizing for speed or performance. A technical stakeholder may be primarily concerned with long-term maintainability or security exposure. Each of these perspectives is rational, but they don’t naturally align. As I wrote in Modern B2B Marketing, every stakeholder involved in a B2B purchase must be convinced of three things: 1) that they have a need, 2) that the solution addresses it in a unique way, and 3) that they need it now. Getting seven to ten committee members to arrive at all three convictions simultaneously is where most deals break down.

When marketing speaks to “the buyer” as if these tensions don’t exist, it inadvertently creates content that fails to genuinely resonate with any member of the committee. Worse, it can amplify internal disagreement by validating one role’s priorities while ignoring another’s. The result is paralysis, and it’s almost invisible from the outside.

Why marketing needs to understand decision mechanics

This is where most teams fail. They treat buying committee dynamics as a sales problem, something to be solved at the deal stage. But by the time a buying committee is actively negotiating tradeoffs, many of the underlying narratives have already been set.

Marketing plays a critical role earlier in the process by shaping how problems are framed, how risk is understood, and what “good” decisions look like for the organization. When marketing fails to account for buying committee dynamics, the messaging may be directionally correct while completely missing how the decision actually gets made.

Strong thought leadership does more than persuade individuals. As I noted in Modern B2B Marketing, prospects consume five to seven pieces of content during their journey toward engaging with vendors, and many of those interactions happen outside the vendor’s website. When each committee member is independently consuming that much content with different concerns, aligning the group requires more than good messaging aimed at a single profile. Thought leadership helps groups align by providing a shared language for trade-offs and outcomes. That kind of alignment doesn’t happen by accident. It requires an explicit understanding of who is involved in decisions, how power is distributed, and where friction typically emerges. This is where persona-based thinking needs to give way to decision-based thinking.

From personas to decision reality

Instead of asking “Who is our buyer?” the more useful question is “What has to be true inside the organization for a decision to happen?” Answering that question requires mapping the buying committee as a system and treating it as an interconnected set of roles rather than a collection of isolated profiles. It means understanding authority alongside preferences, and surfacing the risks people are unwilling to own, along with the objections they’ll voice openly.

When teams do this work explicitly, the picture quickly changes. They see why certain messages resonate with some stakeholders and quietly alarm others. They understand why champions struggle to build momentum internally, even when they are personally convinced. And they gain visibility into the specific tensions that must be resolved for consensus to form. This clarity doesn’t guarantee a closed deal, but it does reduce the number of deals that drift into no-decision outcomes, and it gives marketing and sales a shared map for how to move a committee forward.

The buying committee decision mapping workflow

What follows is the full workflow used to make buying committee dynamics explicit and reusable. It builds on existing market, customer, and competitive insights to create a shared map of how decisions actually happen within target accounts. The workflow focuses on identifying who holds real authority, surfacing the unspoken risks that cause stalls, and translating those dynamics into content and messaging that help committees reach consensus rather than default to inaction.

Where this leads

I’ve watched teams spend months producing content that speaks eloquently to a buyer who doesn’t exist in isolation. The CFO reads it and worries about cost exposure, while the VP of Engineering reads it and worries about migration risk. The line-of-business leader reads it and wonders who will own the rollout, and everyone nods without moving forward.

Buying committees don’t need more content. They need help getting to a decision that everyone can live with, and that work starts with making the mechanics of the decision visible. Marketing is uniquely positioned to do it.

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About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Buying Committee Decision Mapping Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow maps how B2B buying decisions actually get made inside target accounts.

It identifies:

• Who is involved in the decision

• Who has power vs influence

• Where decisions stall or die

• What alignment must exist for a “yes”

This workflow does not create content or personas.

It creates decision mechanics.

It answers one question:

“What has to be true inside the buying committee for a decision to happen?”

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Identify Buying Committee Roles

• Step 2: Map Decision Power & Influence

• Step 3: Define Role-Level Success Criteria

• Step 4: Identify Decision Friction & Stall Points

• Step 5: Produce the Decision Map
  

The workflow was created by prompts.tinytechguides.com (https://prompts.tinytechguides.com) and builds on these existing workflows:

- Prompt Workflow: Voice of Customer

- Prompt Workflow: Competitive Landscape Mapping

- Strategic Battlecard Workflow

- Monthly Market Signal Synthesis Workflow

Step 0: Define inputs

Before running the workflow, gather these inputs:

- {account_type} = target account profile or segment

- {deal_examples} = recent won, lost, and stalled deals

- {sales_inputs} = call notes, objections, deal commentary

- {customer_inputs} = outputs from Voice of Customer workflow

- {competitive_context} = outputs from Competitive Landscape Mapping

- {business_context} = company priorities for this segment

Step 1: Identify buying committee roles

B2B purchases are rarely made by a single decision-maker. Multiple roles participate, each entering the process at different stages with different incentives. This prompt surfaces who is actually in the room.

**Prompt:**

# Role

You are a B2B strategist analyzing how buying decisions actually happen inside real accounts.

# Context

B2B purchases are rarely made by a single decision-maker. Multiple roles participate, each entering the process at different stages with different incentives.

# Task

Using {deal_examples}, {sales_inputs}, and {customer_inputs}, identify all roles typically involved in buying decisions for {account_type}.

# Format

For each role:

- Role name (functional, not persona-based)

- Core responsibility in the organization

- Typical involvement stage (early / mid / late)

# Tone

Grounded, factual, and based on observed deal behavior.

Step 2: Map decision power and influence

Some roles formally approve decisions, others influence outcomes informally, and some can block progress without ever owning the final call. Understanding these dynamics is where most persona work falls short.

**Prompt:**

# Role

You are diagnosing power dynamics inside buying committees.

# Context

Some roles formally approve decisions, others influence outcomes, and some can block progress without owning the final call.

# Task

For each buying committee role, assess decision authority, influence, and blocking power.

# Format

For each role:

- Decision power (High / Medium / Low)

- Influence level (High / Medium / Low)

- Ability to block the decision (Yes / No)

- Notes on how this role typically exercises power

# Tone

Analytical, unsentimental, and realistic.

Step 3: Define role-level success criteria

Buying committee members optimize for different outcomes and are accountable for different risks. These differences are the root of most internal conflict, and most stalled deals.

**Prompt:**

# Role

You are uncovering how each role defines a successful outcome.

# Context

Buying committee members optimize for different outcomes and are accountable for different risks. These differences often drive internal conflict.

# Task

Define what success and failure look like for each role in the context of this decision.

# Format

For each role:

- Primary objective

- Key risks or fears

- What failure would look like to them

- Conditions under which they feel safe supporting the decision

# Tone

Empathetic but pragmatic.

Step 4: Identify decision friction and stall points

Most stalled deals fail because of unresolved internal tensions rather than product deficiencies. This step makes those tensions visible before they kill the deal.

**Prompt:**

Read more

I spent the first half of my career as a practitioner, designing data warehouses, building predictive models, and deploying BI systems at IBM. The second half has been on the dark side of product marketing at SAS, Dell, TIBCO, Alteryx, and Alation, where I’ve tried to explain those same technologies to prospective buyers. Since then, I’ve had the opportunity to apply my trade as a full-time PMM consultant and advisor to startups and behemoths alike.

Both sides taught me the same lesson. The best content teams produce less.

That sounds backwards because every marketing leader I know is under pressure to publish more blogs, more social posts, and more thought leadership. AI has made it easy to crank out content at scale. Unfortunately, most of it is garbage.

I wrote about this a couple of years ago in a piece called “AI Entropy: The Vicious Circle of AI-Generated Content.” The problem has gotten worse since then. We’re drowning in content that says nothing, written by systems trained on other content that also said nothing. When you train something on nothing, what do you get? The whole industry is racing toward the semantic middle, like a tradeshow buffet full of rubbery chicken that technically counts as food.

Diagnosing this problem is easier than fixing it, and this post is about the fix.

The decision problem

Most B2B content teams have the skills to execute. What they lack is a decision-making process.

They publish before they’ve decided what they believe. They run a trend report, see something interesting, and rush to get a piece out the door. Two weeks later, they’ve moved on, and the piece sits there orphaned like a faded memory, generating nothing.

The teams that build real credibility follow a different pattern. Their content gets cited, shared, and remembered because they move through a specific sequence before publishing.

Discover → Decide → Commit → Validate → Amplify

Most teams skip from discover straight to amplify and then wonder why nothing compounds, why every quarter feels like starting over, and why their thought leadership sounds like everyone else’s. They never decided what they believed. They just published what they found.

A system that forces the hard thinking first

Over the past year, I’ve been building a library of prompt workflows for B2B marketers. There are hundreds of them now, covering everything from persona development to competitive battlecards to analyst briefings. If you’ve read The PMM’s Prompt Playbook, you’ve seen some of this work.

But individual prompts aren’t enough. What most teams need is a system that connects market understanding to published POV to distribution.

Phase 1: Understand the market

Before you can have a point of view, you need to understand what’s happening in your market. Two workflows handle this monthly.

Trend discovery scans for signals like new tools, behavioral shifts, and emerging patterns without jumping to conclusions about what they mean.

Competitive landscape mapping analyzes what competitors are doing and saying, so you understand the landscape before figuring out where to stand.

Both workflows are strictly about gathering raw material for later synthesis.

Phase 2: Decide what matters

Most teams collect signals but never force themselves to prioritize. I watched this happen at Alteryx when we were tracking the analytics automation space. We had dashboards full of competitor moves, analyst reports, and customer feedback. What we didn’t have was a clear answer to the question: what do we actually believe about where this market is headed?

Monthly market signal synthesis takes outputs from trend discovery and competitive mapping and compresses them into one or two clear market narratives. The workflow answers a single question: given everything we know, what should we care about right now?

This step is the upstream governor of the entire system. Getting it wrong means everything downstream becomes noise.

Phase 3: Commit to a point of view

Once you’ve identified a narrative worth pursuing, you need to decide what you believe about it with enough conviction to publish.

Insight to thought leadership takes one market narrative and forces clarity on your POV, your argument, and your boundaries. There’s no writing at this stage, just decision-making about what you’re willing to defend.

Strategic POV validation pressure-tests the POV before you invest in creating the asset. Is this differentiated? Is it defensible? Is it useful to your audience? The workflow ends with a real decision to advance, revise, or kill the idea.

Nothing serious should skip this step. I’ve watched teams spend weeks on whitepapers that should have been killed in the first hour.

Phase 4: Amplify without flattening

Once you’ve created something worth reading, you need to get it in front of people without turning it into sludge.

Thought leadership to social distribution translates a validated piece into platform-native content, expressing one idea in many ways while keeping the same spine throughout.

Content calendar and editorial planning orchestrates cadence, channels, and capacity so content planning stops being reactive.

How this runs in practice

Monthly, you run trend discovery and competitive mapping, then market signal synthesis, and then select one or two narratives. Picking only one or two narratives per month is the constraint that makes everything else work.

For each thought leadership asset, you run the insight-to-thought-leadership workflow, then POV validation. Only after validation do you create the piece itself.

After publication, you run social distribution and let one idea work across channels.

This approach eliminates the scrambling that most teams experience. There’s no more “what should we post this week?” panic, and no more orphaned content sitting on your blog with no amplification plan.

Why this approach works

The system does two things that most content operations fail to do. First, it forces you to make decisions before you start writing. You can’t skip ahead because each workflow requires outputs from the previous one. Second, it treats judgment and execution as separate skills that happen at different times with different inputs. Most teams blur these together, which is how you end up with beautifully written pieces that have nothing to say.

The result is leverage. One validated POV validated across channels will outperform ten mediocre posts. I’ve seen small teams consistently outperform much larger ones by following this principle: their smaller volume of content carried real conviction, and their audience could tell the difference.

The bottom line

You don’t need more content. You need fewer ideas, stronger conviction, and better sequencing.

That’s what this system is for.

The workflows referenced above are available on TinyTechGuides. If you’re building a content operation and want to talk through implementation, find me on LinkedIn.

Share B2B Marketing Prompts by TinyTechGuides

Leave a comment

About David Sweenor

Books: Artificial Intelligence | Generative AI Business Applications | The Generative AI Practitioner’s Guide | The CIO’s Guide to Adopting Generative AI | Modern B2B Marketing | The PMM’s Prompt Playbook

Founder of TinyTechGuides, David Sweenor is a top 25 analytics and AI thought leader and influencer, international speaker, consultant and advisor, and acclaimed author with several patents. He is a product marketing leader, analytics practitioner, and specialist in the business application of AI, ML, data science, IoT, and business intelligence.

With over 25 years of hands-on business analytics experience, Sweenor has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest, in advanced analytic roles.

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn https://www.linkedin.com/in/davidsweenor/.

Need help with PMM? Let me know.

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Thought Leadership → Social POV Distribution Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow translates a validated long-form thought leadership asset (such as a whitepaper or flagship POV piece) into short-form social content while preserving the original thinking, argument, and intent.

It does not summarize.
It does not flatten ideas into marketing fluff.
It re-expresses the same POV across platforms in a way that is native, credible, and high-signal.

This workflow answers:

“How do we extend the reach of serious thinking without diluting it?”

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Extract Core Arguments & POV

• Step 2: Identify Social-Native Angles

• Step 3: Translate POV Into Platform Formats

• Step 4: Preserve Narrative Consistency

• Step 5: Finalize & Queue for Distribution

Step 0: Define Inputs

{source_asset} = final whitepaper or long-form thought leadership piece

{pov_statement} = validated POV from the Strategic POV Validation Workflow

{target_platforms} = social platforms for distribution

{audience} = intended social audience

{author_voice} = tone and perspective to preserve

{distribution_goal} = awareness, authority, engagement, traffic, etc.

Referenced workflows:

https://prompts.tinytechguides.com/p/not-every-point-of-view-deserves

Step 1: Extract Core Arguments & POV

Goal:
Identify the intellectual spine of the thought leadership asset.

# Role

You are an editorial translator extracting core ideas from long-form thought leadership.

# Context

Short-form content must preserve the same thinking, logic, and conclusions as the original asset.

# Task

Review {source_asset} and extract the core POV, key arguments, and conclusions that must be preserved across all derivatives.

# Format

- Core POV (1 sentence)

- 3–5 key arguments

- One non-negotiable takeaway

# Tone

Faithful, precise, and analytical.

Step 2: Identify Social-Native Angles

Goal:
Determine how the POV should enter each platform.

# Role

You are a social strategist aligning ideas to platform behavior.

# Context

Each platform rewards different entry points. The POV stays constant; the framing changes.

# Task

For each platform in {target_platforms}, identify the strongest social-native angle to introduce the POV to {audience}.

# Format

For each platform:

- Platform name

- Primary angle or hook

- Supporting tension or insight

- Intended reader reaction

# Tone

Platform-aware, strategic, and audience-centric.

Step 3: Translate POV Into Platform Formats

Goal:
Create high-signal social content that stands on its own.

# Role

You are a content creator translating thought leadership into short-form social posts.

# Context

Each post should deliver real value even if the reader never clicks through to the source asset.

# Task

Write platform-appropriate posts that express the POV and supporting arguments without oversimplifying the idea.

# Format

For each platform:

- Post text

- Optional CTA (if appropriate)

- Link placement guidance (if used)

# Tone

Clear, confident, and substance-first.

Step 4: Preserve Narrative Consistency

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Strategic POV Validation Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow pressure-tests a thought leadership point of view before it is written or distributed.

Its job is to ensure a POV is:

• Differentiated (not obvious or generic)

• Defensible (you can stand behind it)

• Useful (it changes how the reader thinks or acts)

This workflow does not improve writing.
It decides whether the idea deserves amplification at all.

This workflow answers:

“Is this POV strong enough to represent us publicly?”

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Test for Differentiation

• Step 2: Test for Defensibility

• Step 3: Test for Usefulness

• Step 4: Identify Risks & Weaknesses

• Step 5: Decide: Advance, Revise, or Kill

Step 0: Define Inputs

• {pov_statement} = the POV defined in the Insight → Thought Leadership Workflow

  https://open.substack.com/pub/davidsweenor/p/thought-leadership-starts-with-a

• {argument_outline} = supporting arguments and evidence

• {audience} = intended reader

• {business_context} = company positioning, strategy, and constraints

• {publication_channel} = where this POV will be published

• {risk_tolerance} = conservative, balanced, or bold

Step 1: Test for Differentiation

Goal:
Ensure the POV is not obvious, consensus-driven, or already saturated.

# Role

You are a critical editor evaluating market differentiation.

# Context

Thought leadership that restates common beliefs fails to create authority or memorability.

# Task

Evaluate whether {pov_statement} meaningfully differs from common market narratives aimed at {audience}.

# Format

- Common market belief

- How this POV differs

- Differentiation score (High / Medium / Low)

- Brief justification

# Tone

Skeptical, sharp, and market-aware.

Step 2: Test for Defensibility

Goal:
Confirm the POV can withstand scrutiny.

# Role

You are a strategist stress-testing an argument.

# Context

A POV that cannot be defended damages credibility, even if it attracts attention.

# Task

Assess whether the {argument_outline} sufficiently supports {pov_statement}, considering counterarguments and edge cases.

# Format

- Strongest supporting evidence

- Likely counterarguments

- How well the POV holds up (Strong / Moderate / Weak)

- Gaps that need reinforcement

# Tone

Rigorous, fair, and intellectually honest.

Step 3: Test for Usefulness

Goal:
Ensure the POV creates value for the reader.

# Role

You are evaluating reader impact.

# Context

Thought leadership should change how a reader thinks, decides, or acts — not just inform them.

# Task

Evaluate how {pov_statement} helps {audience} make better decisions or see a problem differently.

# Format

- What the reader likely believes today

- What they should believe after reading

- Practical implications for the reader

- Usefulness score (High / Medium / Low)

# Tone

Reader-centric, practical, and outcome-focused.

Step 4: Identify Risks & Weaknesses

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Insight → Thought Leadership Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow turns a validated market insight into a clear, opinionated thought leadership position that is ready to be written and published.

It does not write the article.
It decides the idea, the argument, and the boundary.

This workflow answers:

“What do we believe strongly enough to put our name on?”

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Select the Core Insight

• Step 2: Define the Point of View (POV)

• Step 3: Build the Argument Structure

• Step 4: Set Editorial Boundaries

• Step 5: Produce a Publish-Ready Outline

Step 0: Define Inputs

• {source_insight} = core insight or narrative from Monthly Market Signal Synthesis

https://open.substack.com/pub/davidsweenor/p/what-matters-this-month

• {audience} = who this thought leadership is for

• {business_context} = why this idea matters to the business

• {author_perspective} = founder, PMM, operator, practitioner, etc.

• {publication_channel} = primary publishing destination (e.g. Substack)

• {objective} = what this piece should change in the reader’s thinking

Step 1: Select the Core Insight

Goal:
Choose one insight worth developing — not a bundle.

# Role

You are an editorial strategist selecting a single idea to develop into thought leadership.

# Context

Strong thought leadership focuses on one clear insight. Combining ideas weakens conviction and clarity.

# Task

Review {source_insight} and select the single insight that is most relevant to {audience} and {business_context}.

# Format

- Selected insight (1–2 sentences)

- Why this insight matters now

- Why this audience should care

# Tone

Focused, deliberate, and confident.

Step 2: Define the Point of View (POV)

Goal:
Turn insight into an opinion, not an observation.

# Role

You are defining a clear, defensible point of view.

# Context

Insight becomes thought leadership only when it expresses a belief or stance that others may not agree with.

# Task

Translate the selected insight into a clear POV statement that reflects {author_perspective} and serves {objective}.

# Format

- POV statement (one clear sentence)

- Common belief this POV challenges

- What someone who agrees will now do differently

# Tone

Opinionated, grounded, and assertive without being inflammatory.

Step 3: Build the Argument Structure

Goal:
Ensure the POV can be supported, not just asserted.

# Role

You are an editor structuring a persuasive argument.

# Context

A strong POV requires a logical argument that guides the reader from problem to conclusion.

# Task

Outline the argument that supports the POV using evidence, reasoning, and examples.

# Format

- Core thesis

- 3–5 supporting arguments

- Evidence or examples for each argument

- Implications if the POV is true

# Tone

Clear, logical, and persuasive.

Step 4: Set Editorial Boundaries

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Monthly Market Signal Synthesis Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow synthesizes pre-researched market, trend, and competitive inputs into 1–2 clear market narratives that guide content, positioning, and GTM decisions for the month. It does not perform raw discovery.

Instead, it consumes outputs from existing research workflows (already in Substack) and focuses purely on sensemaking, prioritization, and narrative clarity.

This workflow answers:

“Given everything we already know, what actually matters right now?”

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Review Research Outputs

• Step 2: Cluster Signals Into Patterns

• Step 3: Pressure-Test Signal Relevance

• Step 4: Synthesize Core Market Narratives

• Step 5: Document & Distribute Insights

Step 0: Define Inputs

• {time_period} = month or date range being analyzed

• {market_scope} = industry, segment, or category focus

• {trend_inputs} = summarized outputs from:

• {competitive_inputs} = summarized outputs from:

• {customer_inputs} = VOC summaries (calls, tickets, interviews, notes)

• {business_context} = internal priorities or GTM focus

• {audience} = who this synthesis is for (PMM, content, execs, etc.)

Important constraint:
Inputs should be synthesized findings, not raw links, transcripts, or research dumps.

Step 1: Review Research Outputs

Goal:
Establish shared context before interpretation begins.

# Role

You are a B2B strategist reviewing existing market research outputs.

# Context

Trend and competitive research has already been conducted using dedicated workflows. This step ensures those findings are understood before synthesis begins.

# Task

Review {trend_inputs}, {competitive_inputs}, and {customer_inputs}. Summarize the most salient observations without adding interpretation.

# Format

Three sections:

- Trend highlights

- Competitive highlights

- Customer highlights

# Tone

Neutral, factual, and observational.

Step 2: Cluster Signals Into Patterns

Goal:
Move from isolated findings to emerging patterns.

# Role

You are a strategist identifying patterns across validated research outputs.

# Context

Patterns emerge when multiple signals point in the same direction across trends, customers, and competitors.

# Task

Group related observations into signal clusters that suggest emerging shifts, tensions, or opportunities.

# Format

For each cluster:

- Cluster name

- Signals included

- Early hypothesis about what this cluster suggests

# Tone

Analytical and exploratory.

Step 3: Pressure-Test Signal Relevance

Goal:
Filter out interesting noise.

# Role

You are a senior marketer prioritizing focus.

# Context

Not every pattern deserves attention. This step ensures alignment with business reality.

# Task

Evaluate each signal cluster against {business_context} and {audience}. Decide which clusters matter now.

# Format

For each cluster:

- Relevance (High / Medium / Low)

- Reasoning

- Recommended action (advance, monitor, discard)

# Tone

Pragmatic and decisive.

Step 4: Synthesize Core Market Narratives

Read more

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Content Calendar & Editorial Planning Workflow

Created by prompts.tinytechguides.com

What This Workflow Does

This workflow helps a B2B marketing team (or a single, brave IC) create a clear, strategy-led content calendar for a defined planning period. It turns goals, audiences, and themes into a realistic publishing plan that aligns content, channels, and capacity — without chaos, guessing, or last-minute scrambles.

This workflow decides:

• What to publish

• Why it matters

• Where it goes

• When it ships

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Establish Planning Horizon & Content Goals

• Step 2: Select Core Themes & Narratives

• Step 3: Map Themes to Content Types & Channels

• Step 4: Sequence Content Into a Calendar

• Step 5: Validate Against Capacity, GTM, and Priorities

• Step 6: Lock, Publish, and Review

Step 0: Define Inputs

• {planning_horizon} = time period covered by the calendar (e.g. 30, 60, 90 days)

• {business_objectives} = primary goals this content should support

• {target_audience} = ICPs, personas, or buying committee

• {core_channels} = where content will be published

• {content_capacity} = realistic production bandwidth

• {key_dates} = launches, events, announcements, deadlines

• {content_pillars} = high-level themes or focus areas

• {success_metrics} = how content success will be evaluated

Step 1: Establish Planning Horizon & Content Goals

Goal:
Align the content calendar to business priorities so content supports real outcomes, not vibes.

# Role

You are a B2B marketing strategist responsible for aligning content planning with business goals.

# Context

Content is most effective when it is intentionally planned around clear objectives, audiences, and timelines. This step ensures the content calendar is anchored to real priorities for the defined planning horizon.

# Task

Using {planning_horizon}, {business_objectives}, {target_audience}, and {success_metrics}, define the primary purpose of content for this period and the outcomes it should support.

# Format

- Planning horizon summary

- Primary content goals (3–5 bullets)

- Audience focus for this period

- Success definition for the calendar

# Tone

Clear, pragmatic, and focused on outcomes over output.

Step 2: Select Core Themes & Narratives

Goal:
Decide what you will talk about before deciding what you will publish.

# Role

You are a content strategist translating business priorities into clear editorial themes.

# Context

Strong content calendars are built around a small number of repeatable themes that reinforce positioning and credibility over time.

# Task

Using {content_pillars}, {business_objectives}, and {target_audience}, identify 3–5 core themes and the key narratives or points of view within each theme.

# Format

For each theme:

- Theme name

- Why it matters to the audience

- Key narrative or POV

- Example topics this theme could produce

# Tone

Strategic, opinionated, and focused on clarity over breadth.

Step 3: Map Themes to Content Types & Channels

Goal:
Ensure each theme shows up in the right formats and places.

# Role

You are a B2B content planner responsible for aligning themes to channels and formats.

# Context

Not all content belongs everywhere. This step ensures themes are expressed through the most effective content types and channels.

# Task

Map each core theme to appropriate content formats and {core_channels}, considering audience behavior and intent.

# Format

Table with columns:

- Theme

- Content type (essay, post, video, etc.)

- Primary channel

- Secondary channel (if any)

- Intended audience action

# Tone

Practical, audience-aware, and execution-ready.

Step 4: Sequence Content Into a Calendar

Read more

This year, you stopped asking AI to write for you. You started asking it to check your work.

The data backs this up. Out of 115 posts and 150,000 words, your most-opened content focused on reviewing landing pages, tightening executive messaging, and validating claims before publication. “Editor” themed posts outperformed “creator” posts by a clear margin.

Meanwhile, this newsletter more than doubled in size. We went from around 135 subscribers to well over 300. And the engagement patterns reveal what brought you here. Competitive intelligence posts hit the highest open rates of any category. You want workflows you can reuse. And you want impactful messaging that win deals.

By the Numbers

• 115 posts (87 newsletters, 28 podcasts)

• ~150,000 words

• 39.1% average open rate

• 125% audience growth

Help support and promote small businesses by sharing this post with a friend.

Top 5 Workflows

These got bookmarked and reused. The Sales Plays and Buyer Personas workflows were reopened 4.5x each. You’re building a reference library over here. (I see you, fellow hoarders.)

1. B2B Prompt Engineering for PMMs: The foundation. Everything else was built on this.

2. Competitive Landscape Mapping: Strategic intel is hard. This made it systematic.

3. Executive Messaging Review & Harmonization: Getting execs to agree on messaging? There’s a workflow for that now.

4. Turn Web Pages into Marketing-Ready Data Sheets: Hours saved, every single time.

5. Landing Page Copy Review: Even the best writers need a second pair of eyes. And AI doesn’t get defensive when you ignore its feedback.

Top 5 Podcasts

Deep conversations with practitioners who’ve made the mistakes so you don’t have to. Note, these Top 5 are based on Substack newsletter open rates, not the number of YouTube views, which paints a different picture.

1. The Customer Hero Principle: Gabriela Contreras (Skyline Marketing) on ruthless audience prioritization and escaping jargon land

2. From “AI-Ready” to AI Reality: Shane Murray (Monte Carlo Data) on why building beats endless planning

3. Your Netflix Moment: Catalina Herrera (Dataiku) on why most AI agent pilots fail

4. Stop Chasing Hallucinations: Hyoun Park (Amalgam Insights) on fixing context gaps in agentic AI

5. The AI Agent Mistake 90% of Leaders Make: Chelsea Wise (Relevance AI) on why learning together beats rushing to implement

Top 5 How-To Guides

The stuff you read between meetings. Short-form guides actually outperformed long-form podcasts this year. Respecting your time pays off.

1. Should You Turn ChatGPT’s Memory On or Off?: The privacy vs. productivity tradeoff we’re all navigating

2. Tutorial: Prompt Engineering for PMMs: The companion piece to the #1 workflow

3. Deep Fake Dave & The Future of AI Personas: AI isn’t just writing anymore. It’s becoming.

4. Marketing Mad Libs: Prompt Variables: Level up with variables

5. Customizing GPTs for Authentic Content: Making AI sound like you

Looking at 2026

My bet for next year is that the conversation moves from “prompt workflows” to “automation.”

Not the “set it and forget it” kind, we’ve all been burned by that promise. I’m talking about automated workflows that run reliably on their own. Agents that check their own work and marketing systems, where you review exceptions instead of every single output.

The real question becomes: how do I trust the output enough to stop babysitting it?

I’ll be honest. I don’t have that figured out. But I’m spending 2026 working on it, and I’ll share what works. And what fails spectacularly.

Which workflow saved you the most time this year? Hit reply and tell me. Your answers shape what gets built next.

P.S. Saturday posts got +2% higher open rates. Turns out you prefer AI content when you’re not trapped in back-to-back meetings.

Leave a comment

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him onLinkedIn.

This prompt is not part of The PMM’s Prompt Playbook which has 30 ready-to-use prompts. If you’re looking for more cut-and-paste prompts, join the Substack! Paid subscribers receive new cut-and-paste prompts every week.

Get the PMM’s Prompt Playbook and Modern B2B Marketing today!

Need help with product marketing or prompts? Let me know.

Workflow Name: Better Together – Partner Messaging & Joint Value Prop Framework

created by prompts.tinytechguides.com

What This Workflow Does

This workflow helps two B2B companies create a unified joint value proposition that clearly communicates how their solutions are stronger together. By analyzing both companies’ core messaging and the technical integration, it builds a shared narrative, packaged in a Joint Solution Brief. A final role-play step simulates real-world sales reactions to validate the messaging before going live.

Workflow Steps Summary

• Step 0: Define Inputs

• Step 1: Extract Core Messaging from Each Company

• Step 2: Identify Integration Value and Market Fit

• Step 3: Craft “1+1=3” Joint Value Proposition

• Step 4: Create Joint Solution Brief

• Step 5: AE Role-Play Review – Gut Check from Both Sides

Step 0: Define Inputs

{company_a_messaging} = Company A’s core messaging (value props, positioning, key differentiators)
{company_b_messaging} = Company B’s core messaging (value props, positioning, key differentiators)
{integration_details} = Technical or functional overview of how the two solutions work together
{target_persona} = Who we’re targeting (e.g., IT Director, CMO, etc.)
{pain_points} = Key pain points this joint solution addresses
{joint_use_case} = Primary use case enabled by the integration
{industry_focus} = Industry or vertical this solution is most relevant for
{cta} = Desired call-to-action (e.g., book a demo, download the whitepaper)

Step 1: Extract Core Messaging from Each Company

Goal: Summarize each company’s key positioning, differentiators, and value props.

#Role
You are a B2B messaging strategist helping distill partner company narratives.

#Context
Company A and Company B are collaborating on a joint solution. We need to understand each company’s standalone messaging to later blend them into a unified value prop.

#Task
Summarize the core messaging of Company A and Company B separately. Highlight their target market, top 2–3 differentiators, and tone of voice.

#Format
Bullet points under each company header. Label each clearly.

#Tone
Neutral, clear, and B2B professional.

Step 2: Identify Integration Value and Market Fit

Goal: Translate technical integration into meaningful customer value and market relevance.

#Role
You are a technical marketing consultant translating integrations into business value.

#Context
We have two integrated solutions. Now we need to define the combined functional value, key pain points solved, and which market segments are most affected.

#Task
Based on the integration details, pain points, and use case, explain the real-world impact of this integration. Who benefits most, and why?

#Format
- Summary paragraph of the integration’s value
- Top 3 pain points solved
- Top 2 market segments that benefit
- 1–2 ideal use cases

#Tone
Clear, consultative, with a “problem-solution” style.

Step 3: Craft “1+1=3” Joint Value Proposition

Goal: Build a unified, compelling value proposition that blends both brands.

Read more

This free prompt workflow was shared with me by Pablo Ylarri during a Product Marketing Alliance meet-up. Thanks, Pablo!

If you have a prompt to share, please do so, and you can be featured in the newsletter!

Workflow Name: Latin American Spanish Translation Workflow (Neutral + Professional)

created by prompts.tinytechguides.com

What this Workflow Does

This workflow guides you through creating clean, neutral, and professional translations of English content into Latin American Spanish. It’s crafted to ensure tone, formatting, and clarity stay intact — perfect for biz or marketing use across all Spanish-speaking countries in Latin America.

Workflow Steps Summary

Steps:

• Step 0: Define Inputs

• Step 1: Establish Translator Role, Guidelines, and Rules

• Step 2: Insert Source Text for Translation

• Step 3: Output Final Translation

Step 0: Define Inputs

{source_text} = Text that needs to be translated

{tone} = Tone of the original text (e.g., professional, friendly, technical)

{formatting_preserved} = Yes/No – should formatting (like bullets, bold, etc.) be preserved

Step 1: Establish Translator Role, Guidelines, and Rules

Goal: Clearly define the translator’s responsibilities, expectations, and style rules.

#Role

You are an expert English-to-Spanish translator specialized in **neutral Latin American Spanish**. You translate content in a way that sounds natural and professional to native Spanish speakers across Latin America — avoiding regionalisms and Spain-specific language.

#Context

You’ve been hired to translate a text into Latin American Spanish. The client requires high accuracy, professionalism, and formatting preservation. You must follow strict rules to ensure the translation is clean, neutral, and ready to use for business, marketing, or professional communications.

#Task

Translate the provided English text into perfect **Latin American Spanish**, maintaining the tone, formatting, grammar, and clarity.

#Rules

- Use **neutral Spanish**, avoiding regionalisms (e.g., no “ordenador”, no “vosotros”, no idioms from Spain)

- Respect proper grammar and capitalization rules of Spanish

- Keep **blank lines, bullet points, lists, and punctuation** as in the original

- Maintain a **professional tone** that sounds **natural to native speakers in Latin America**

- Do **not** translate company names, product names, or URLs

- Do **not** add any commentary — only return the translated text

#Format

Preserve the original formatting exactly: blank lines, bullets, line breaks, bold/italics (if any), punctuation — everything. Deliver only the translated Spanish version.

#Tone

Match the tone of the original text. For this job, the tone is: {tone}

Step 2: Insert Source Text for Translation

Goal: Feed the text into the prompt while reinforcing rules and tone.

#Role

You are continuing your task as a Latin American Spanish translator.

#Context

Below is the English content to be translated. This content should be converted following all guidelines, rules, and formatting expectations outlined above.

#Task

Translate this into professional, **neutral Latin American Spanish**, preserving formatting and tone. Avoid any regionalisms or informal/slang expressions. It should feel smooth and natural to a Latin American audience.

#Rules

- Use **neutral Spanish**, avoiding regionalisms (no “ordenador”, no “vosotros”, no idioms from Spain)

- Follow Spanish grammar and capitalization norms

- Keep formatting as-is (lists, punctuation, line breaks, etc.)

- Maintain professional tone

- Don’t translate company/product names or URLs

- Don’t include commentary — just the translated text

#Format

Return only the translated version of the text. Match the original formatting perfectly.

#Tone

The tone of this text is: {tone}

Text to translate:

{source_text}

Step 3: Output Final Translation

Goal: Deliver the final, client-ready Spanish version of the text.

#Role

You are delivering the final Spanish translation for client use.

#Context

The client expects a clean, final version that sounds fluent and natural in **Latin American Spanish**, free from regional quirks or awkwardness, and styled to match the original English tone.

#Task

Review the translated version to ensure tone, formatting, and grammar are perfect. This is the client-ready version — it should require no edits. Follow the established rules.

#Rules

- Use **neutral Spanish**

- No Spain-specific terms or idioms

- Follow proper grammar and punctuation

- Keep original structure, formatting, and layout

- Exclude translations of brand names, product names, and URLs

- No commentary — just return the translation

#Format

Output only the final translated Spanish text with the exact same formatting.

#Tone

Maintain the tone as: {tone}

Why This Workflow Works

• Embeds clear, enforceable translation rules that prevent localization issues

• Maintains formatting and tone so the output is plug-and-play

• Reduces back-and-forth by delivering polished, rule-following translations

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About David Sweenor

Books: Artificial Intelligence | Generative AI Business Applications | The Generative AI Practitioner’s Guide | The CIO’s Guide to Adopting Generative AI | Modern B2B Marketing | The PMM’s Prompt Playbook

Founder of TinyTechGuides, David Sweenor is a top 25 analytics and AI thought leader and influencer, international speaker, consultant and advisor, and acclaimed author with several patents. He is a product marketing leader, analytics practitioner, and specialist in the business application of AI, ML, data science, IoT, and business intelligence.

With over 25 years of hands-on business analytics experience, Sweenor has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest, in advanced analytic roles.

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn https://www.linkedin.com/in/davidsweenor/.

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