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/unit-economicsWrote 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/unit-economics)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/unit-economics"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/unit-economics/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/unit-economics"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/unit-economics.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 | $0.00090 | $0.00822 |
| Opus 5 | $0.00045 | $0.00411 |
| Sonnet 5 | $0.00018 | $0.00164 |
| Haiku 4.5 | $0.00009 | $0.00082 |
Grade A, and why
unit-economics 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 7d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unit Economics Skill
A business is only viable if each customer is worth more than it costs to acquire and serve. This skill computes the core unit economics — CAC, LTV, the LTV:CAC ratio, payback period, and contribution margin — from real numbers (not vibes), states a clear verdict against the rule-of-thumb benchmarks, and shows which lever moves the model most.
Required Inputs
Ask for these only if they aren't already provided:
- ARPA — average revenue per account, per month (or per period).
- Gross margin % — the share of revenue left after cost-to-serve.
- Churn % — monthly customer (or revenue) churn — drives LTV.
- CAC — fully-loaded cost to acquire a customer (sales + marketing ÷ new customers).
Output Format
Unit Economics: [business]
1. The numbers — computed, with the formula shown (use the helper script so they're consistent):
| Metric | Value | Benchmark |
|---|---|---|
| Lifetime (1/churn) | ||
| LTV (ARPA × margin ÷ churn) | ||
| CAC | ||
| LTV : CAC | ≥ 3:1 healthy | |
| Payback (months) | < 12 healthy | |
| Contribution margin |
2. Verdict — healthy / borderline / underwater, in one line, against the benchmarks (LTV:CAC ≥ 3, payback < 12 months).
3. Biggest levers — which input, improved realistically, moves the model most (usually churn or CAC), with the rough effect.
4. Caveats — where the inputs are assumptions vs. measured, and what to validate before betting on this.
Programmatic Helper
scripts/unit_econ.py (stdlib only) computes the model so the numbers are calculated, not estimated:
# in.json: {"arpa": 50, "gross_margin": 0.8, "monthly_churn": 0.03, "cac": 400}
python3 scripts/unit_econ.py in.json
python3 scripts/unit_econ.py in.json --json
Quality Checks
- LTV uses gross margin, not raw revenue (a common, model-breaking error)
- The numbers are computed by the helper, not eyeballed
- Verdict is stated against the standard benchmarks (LTV:CAC ≥ 3, payback < 12mo)
- The biggest lever is identified with its rough effect
- Assumed inputs are flagged separately from measured ones
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.
- 7d ago First seen · 73 lines · 90 tokens per session scan A 35bd09c9346e
unit-economics is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 822 once invoked, about $0.0005 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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