Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill measuring-product-market-fitgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/measuring-product-market-fit)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/measuring-product-market-fit"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/measuring-product-market-fit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/measuring-product-market-fit"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/measuring-product-market-fit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01210 |
| Opus 5 | $0.00026 | $0.00605 |
| Sonnet 5 | $0.00010 | $0.00242 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
measuring-product-market-fit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measuring Product-Market Fit
Help the user assess and achieve product-market fit using frameworks from 46 product leaders.
How to Help
When the user asks about product-market fit:
- Understand their stage - Ask how many customers they have, what their retention looks like, and what signals they're seeing (or not seeing)
- Diagnose the situation - Determine if they're confusing vanity metrics with PMF, if they have PMF in a specific segment, or if they're clearly pre-PMF
- Apply the right framework - Help them use the Sean Ellis survey, retention curves, or reference customer counts depending on their situation
- Guide next steps - Help them decide whether to scale or continue iterating based on the evidence
Core Principles
Use the Sean Ellis "disappointment" survey
Sean Ellis: "How would you feel if you could no longer use this product? Very disappointed, somewhat disappointed, or not disappointed. If 40% say 'very disappointed,' you're on the right track." This is a leading indicator of PMF before long-term retention data is available. Focus on the "very disappointed" segment as your core value indicator.
Retention is the ultimate metric
Uri Levine: "Product market fit has one metric. Retention. If you create value, they will come back. If they're not coming back, you're not creating value." Look for retention curves that flatten over time rather than decaying to zero. The "smile curve" - where engagement increases over time - is the strongest signal.
PMF is obvious when you have it
Matt MacInnis: "Product market fit is something where you absolutely know it when you see it. Therefore if you don't absolutely know it, you don't have it." If there's doubt, you likely don't have it. Look for the market pulling the product out of your hands.
PMF is not static - it can be lost
Casey Winters: "Protecting what you've built is increasingly important once you build scale. You might fall out of product market fit in a year or five years if you're not continually making your product better." Markets shift, competitors improve, and user expectations rise.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 78 lines · 52 tokens per session scan A 27e9eb992068
measuring-product-market-fit is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 1,210 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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