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 liqiongyu/lenny_skills_plus --skill measuring-product-market-fitgit clone --depth 1 https://github.com/liqiongyu/lenny_skills_plusWrote 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/liqiongyu/lenny_skills_plus/measuring-product-market-fit)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/measuring-product-market-fit"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/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/liqiongyu/lenny_skills_plus/measuring-product-market-fit"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/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 | $0.00038 | $0.02499 |
| Opus 5 | $0.00019 | $0.01249 |
| Sonnet 5 | $0.00008 | $0.00500 |
| Haiku 4.5 | $0.00004 | $0.00250 |
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 12d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measuring Product-Market Fit
Scope
Covers
- Measuring PMF using a triangulated signal set (survey + behavior + customer evidence)
- Running and interpreting the Sean Ellis "Very Disappointed" survey (overall + by segment)
- Reading retention curves / cohort retention as PMF evidence (and knowing when they mislead)
- Using reference-customer / advocacy signals as an additional PMF proxy
- Detecting PMF drift (market shifts, rising expectations, competitive resets) and setting a re-measurement cadence
- Special handling for marketplaces (measure PMF per side; focus on the "hard side" first)
When to use
- "Do we have PMF? For which segment?"
- "Run a Sean Ellis PMF survey and tell me what it means."
- "Build a PMF scorecard with retention + survey + references."
- "Our market shifted—did we lose PMF?"
- "We want a go/no-go signal for scaling growth spend or launching publicly."
When NOT to use
- You haven’t defined the problem/ICP yet (use
problem-definition). - You only need a survey instrument, not a full PMF measurement system (use
designing-surveys). - You’re deciding whether/how to pivot (use
startup-pivoting) rather than measuring PMF signals. - You need a product vision/strategy doc as the primary output (use
defining-product-vision/ai-product-strategy). - You already have PMF and need to optimize retention or engagement (use
retention-engagement); this skill measures PMF, not post-PMF growth levers. - You need to brainstorm or validate new startup ideas (use
startup-ideation); this skill assumes a product already exists with real users. - You want to define or refine a north-star metric for an established product (use
writing-north-star-metrics); this skill uses metrics as PMF evidence, not as a metric-design exercise.
Inputs
Minimum required
- Product + category + current stage (pre-PMF / early PMF / growth / mature)
- Business model: B2B / B2C / marketplace (and, for marketplaces, which side you’re focusing on)
- Your current best guess at the target segment/ICP (and any meaningful segments)
- Definition of active user and the core value moment (the action that indicates value received)
- What data you can access: survey channels, product analytics, retention cohorts, revenue, qualitative feedback, reference customers/testimonials
- Time horizon and constraints (deadline, privacy/PII constraints, internal-only vs shareable)
What ships with it
13 files 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.
- eval/eval_config.json 931 B
- eval/SHOWCASE.md 5.4 KB
- eval/with_skill.md 29 KB
- eval/without_skill.md 15 KB
- README.md 1.7 KB
- references/CHECKLISTS.md 2.0 KB
- references/EXAMPLES.md 1.8 KB
- references/INTAKE.md 2.5 KB
- references/RUBRIC.md 4.5 KB
- references/SOURCE_SUMMARY.md 1.4 KB
- references/TEMPLATES.md 4.0 KB
- references/WORKFLOW.md 4.4 KB
- skillpack.json 396 B
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.
- 12d ago First seen · 158 lines · 38 tokens per session scan A 7d98c9e66ecb
measuring-product-market-fit is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 2,499 once invoked, about $0.0002 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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