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/fire-numberWrote 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/fire-number)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/fire-number"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/fire-number/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/fire-number"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/fire-number.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 | $0.00098 | $0.00967 |
| Opus 5 | $0.00049 | $0.00483 |
| Sonnet 5 | $0.00020 | $0.00193 |
| Haiku 4.5 | $0.00010 | $0.00097 |
Grade A, and why
fire-number 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FIRE Number Skill
Every FIRE calculation is three assumptions wearing a number's clothing: a withdrawal rate, a real return, and the pretense that returns arrive in a convenient order. This skill does the math properly and refuses the false precision — the deliverable is a surface of outcomes with the assumptions labeled, not a single date to organize a life around.
What This Skill Produces
- The FIRE number — annual spend ÷ withdrawal rate, with the withdrawal rate named as the choice it is
- Years to target — at stated savings, contributions, and real return
- The sensitivity grid — years-to-target across return (3/5/7%) × withdrawal rate (3/3.5/4%)
- The ignored-risks list — sequence-of-returns, taxes, spending drift — stated, not buried
Required Inputs
Ask for these if not provided:
- Current invested savings (invested — not home equity, not emergency cash)
- Monthly contribution (realistic, not aspirational — ask which)
- Target annual spend in retirement (today's dollars; if unknown, current spend is the honest starting guess, labeled)
- Return and withdrawal assumptions (defaults: 5% real, 4% withdrawal — both labeled as defaults)
Programmatic Helper
python3 scripts/fire_number.py --savings 120000 --monthly 3000 --spend 60000
python3 scripts/fire_number.py --savings 120000 --monthly 3000 --spend 60000 --return 5 --wr 4 --json
Deterministic monthly compounding at a constant real return (inflation already removed — never stack an inflation adjustment on top). The script prints the sensitivity grid and its own not-modeled list.
Framework: The Honesty Rules
- The 4% rule is a study, not a law — one country, one era, 30-year horizons; early retirees have longer horizons, which is why the grid includes 3% and 3.5%
- Sequence risk is unmodeled and largest near the finish — a crash in year 1 of retirement ≠ a crash in year 20; say this every time
- Real vs nominal discipline — everything here is in today's dollars; mixing in nominal market returns (~+3%) silently is the classic error
- A range is the deliverable — "17–24 years depending on returns" is honest; "August 2043" is astrology with a spreadsheet
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 · 79 lines · 98 tokens per session scan A 69dee084e535
fire-number is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 967 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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