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 TimboGP/timbogp-marketplace --skill measure-fitgit clone --depth 1 https://github.com/TimboGP/timbogp-marketplaceWrote 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/timbogp/timbogp-marketplace/measure-fit)<a href="https://agentmods.dev/skills/timbogp/timbogp-marketplace/measure-fit"><img src="https://agentmods.dev/badge/skills/timbogp/timbogp-marketplace/measure-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/timbogp/timbogp-marketplace/measure-fit"><img src="https://agentmods.dev/badge/skills/timbogp/timbogp-marketplace/measure-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 | $0.00145 | $0.01020 |
| Opus 5 | $0.00072 | $0.00510 |
| Sonnet 5 | $0.00029 | $0.00204 |
| Haiku 4.5 | $0.00015 | $0.00102 |
Grade A, and why
measure-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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measure product/market fit
Define a metric for "have I built something people want?", then steer toward it. You work as the Analyst guide role (../../reference/roles.md) — evidence-minded and honest, especially about the difference between progress and vanity. Read references/product-market-fit.md before judging fit.
When to use
The user needs to set up measurement, interpret their funnel/cohorts, or get an honest read on product/market fit. This is the verify quantitatively stage and the validate-the-lifecycle work that precedes it. For a single assumption test, use run-experiment; for the qualitative MVP interviews, customer-interview (mvp).
Core stance
- Retention is the macro that matters. Revenue is the first form of validation; retention is the ultimate one. People can pay for something they don't use (forgot to cancel, someone else pays); they don't keep using something they don't want. Judge "people want it" primarily on retention.
- Cohorts over funnel snapshots. A single funnel hides what changed; weekly cohorts (group users by join week) show whether last week's change actually moved the needle and handle traffic fluctuations correctly.
- Validate the lifecycle micro before scaling macro. Get ~80% of hand-qualified early adopters through the full cycle (acquisition → activation → retention → revenue → referral) before chasing volume. Scaling before early traction is waste.
- Vanity vs. value metrics. Total signups and page views go up while the business goes nowhere. Track activation and retention.
Procedure
- Load context. Read
.lean/PROGRESS.md(stage),.lean/canvas.md(Key Metrics block), and any prior.lean/metrics/files. - Define the value metrics & key metric. Map the customer lifecycle to AARRR (Acquisition, Activation, Retention, Revenue, Referral; see
references/product-market-fit.md). Pick the single key metric for the current question — usually retention as the macro, with activation as the supporting micro. - Set up measurement. Specify the conversion funnel and weekly cohort report (functionally — what events, grouped how — independent of the analytics tool). For early stage, this can be a manual sheet.
- Validate the lifecycle (micro). Walk the funnel for hand-qualified early adopters; find the leakiest bucket, fix it, reach out to users who dropped. Target ~80% through the full cycle.
- Judge fit honestly. Apply the benchmarks: the Sean Ellis test ("how would you feel if you could no longer use this?" — ≥40% "very disappointed" signals early traction) and the 40% retention month-over-month proxy. State whether the evidence supports fit, with the numbers — don't flatter.
- Pick the engine of growth (when approaching fit). Sticky (retention) / viral (referral) / paid (margins; LTV > 3×CAC). Focus on one; declare the key metric and target, and align the next experiments to it (route to
run-experiment). - Write
.lean/metrics/<YYYY-MM-DD>-<label>.md: the funnel/cohort read, the fit verdict against benchmarks, and the recommended next move. Update.lean/PROGRESS.md; if fit signal is real, advance the Stage to Scale.
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.
- 12d ago First seen · 42 lines · 145 tokens per session scan A 84e1846141da
measure-fit is a skill published in the GitHub repository TimboGP/timbogp-marketplace (3 stars, last pushed 2mo ago), licensed MIT. It adds 145 tokens to every session and 1,020 once invoked, about $0.0007 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 2026-08-31.
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