PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add rules/mohitagw15856/pm-claude-skills/cohort-curve-modelWrote 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/rules/mohitagw15856/pm-claude-skills/cohort-curve-model)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/cohort-curve-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/cohort-curve-model/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/rules/mohitagw15856/pm-claude-skills/cohort-curve-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/cohort-curve-model.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 | $0.00111 | $0.00945 |
| Opus 5 | $0.00056 | $0.00473 |
| Sonnet 5 | $0.00022 | $0.00189 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
cohort-curve-model 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 8d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cohort Curve Model
Retention data has a shape, and the shape is the business. This skill fits the standard consumer-retention power curve r(t) = a·t^(−b) to observed cohort data by log-log least squares — actual arithmetic run by the bundled script, not model vibes — then projects it forward and prices it.
Required Inputs
- Observed retention by period — from period 0 (100%) through at least period 3-4. Percent or fraction, either works. More periods = a trustworthy fit; 4 is the floor.
- ARPU per period (optional) — revenue per retained user per period. Without it, LTV is reported in lifetime-period multiples instead of currency.
- Projection horizon (optional, default 24 periods).
If the requester has cohort tables (rows of cohorts × months), take the average by period-age or fit the most recent complete cohort — say which you did.
Output Format
- The fit — a (scale), b (decay), R² of the log-log fit, and the observed tail floor. Interpret b plainly: b < 0.5 = strong flattening, a habit is forming; 0.5–1 = normal decay; b > 1 = leaky bucket, the curve never accumulates a base.
- The projection — observed vs fitted by period, marked where observation ends and projection begins.
- The money — lifetime periods (Σ fitted retention over the horizon) and LTV = ARPU × lifetime periods.
- The caveat that matters most — if R² < 0.9, say the power family fits poorly and the projection should be distrusted beyond the observed tail.
Programmatic Helper
This skill ships scripts/cohort_model.py — zero dependencies (stdlib zip+XML). The math and the workbook both come from the script; run it rather than computing by hand:
python3 scripts/cohort_model.py fit cohorts.xlsx --observed '[100,62,48,41,37,34,32]' --arpu 40 --horizon 24
It prints the fit (a=0.619 b=0.371 R²=1.000 lifetime≈7.7 periods LTV≈308) and writes an .xlsx with a Model sheet (parameters + an editable ARPU cell wired to LTV by a live formula) and a Curve sheet (observed vs fitted vs projected). Requires a code-execution environment.
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.
- 8d ago First seen · 51 lines · 111 tokens per session scan A fdaae16476a0
cohort-curve-model is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 945 once invoked, about $0.0006 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 2026-09-03.
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