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/tornado-sensitivityWrote 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/tornado-sensitivity)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/tornado-sensitivity"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/tornado-sensitivity/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/tornado-sensitivity"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/tornado-sensitivity.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 | $0.00100 | $0.00769 |
| Opus 5 | $0.00050 | $0.00385 |
| Sonnet 5 | $0.00020 | $0.00154 |
| Haiku 4.5 | $0.00010 | $0.00077 |
Grade A, and why
tornado-sensitivity 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 6d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tornado Sensitivity
Every model has four drivers people argue about and one that actually controls the answer — usually not the same one. The tornado ranks them: hold everything at base, swing one driver to its low and high, measure the output range, sort. Diligence goes to the top bar; the bottom bars stop hijacking meetings.
Required Inputs
- The model — output name, a formula over named drivers (arithmetic + min/max/abs/sqrt/log/exp only), and per-driver low/base/high. The lows and highs should be defensible bounds ("the worst quarter we've seen", "the vendor's contractual ceiling"), not ±10% ritual.
- If the requester has a spreadsheet instead of a formula: extract the output cell's driver chain into a formula first, and show it for confirmation.
Output Format
- The tornado table — drivers sorted by output swing, with input range, output at each end, and share of total swing. The top driver's share is the headline ("lifetime owns 33% of the uncertainty").
- The meeting verdict — one paragraph: what deserves diligence, what deserves a decision-and-move-on, and any driver whose bounds are the real problem (huge swing because nobody actually knows the range).
- The interaction caveat — one-at-a-time ignores correlated drivers; if two move together in reality (price and churn), say so and model the pair as one driver.
Programmatic Helper
Ships scripts/tornado.py — zero dependencies, with a restricted evaluator (driver names + six math functions; anything else is rejected — injection-tested):
python3 scripts/tornado.py run tornado.xlsx --model model.json
Prints base=1.371 · top driver: lifetime (swing 1.097, 33% of total) and writes Summary + Tornado sheets. Requires a code-execution environment.
Quality Checks
- Swings computed by the script, quoted — never reasoned in prose
- Bounds provenance is stated per driver (measured / contractual / guess) — a tornado of guesses is honestly labelled one
- Share-of-swing sums are shown so the ranking's decisiveness is visible
- Correlated drivers are named and the caveat applied to them specifically
- The verdict names what to STOP arguing about — the negative guidance is half the value
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.
- 6d ago First seen · 47 lines · 100 tokens per session scan A 15797f1910d9
tornado-sensitivity is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 2d ago), licensed MIT. It adds 100 tokens to every session and 769 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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