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/runway-monte-carloWrote 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/runway-monte-carlo)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/runway-monte-carlo"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/runway-monte-carlo/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/runway-monte-carlo"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/runway-monte-carlo.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 | $0.00092 | $0.00890 |
| Opus 5 | $0.00046 | $0.00445 |
| Sonnet 5 | $0.00018 | $0.00178 |
| Haiku 4.5 | $0.00009 | $0.00089 |
Grade A, and why
runway-monte-carlo 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.
Runway Monte Carlo
"Cash divided by burn" is one path through a fan of thousands. Real burn wobbles, revenue growth compounds or doesn't, and the difference between the median path and the unlucky-decile path is the difference between a calm raise and a bridge round. This skill runs the simulation — thousands of paths, actual random draws by the bundled script — and reports runway the way it actually behaves: as percentiles.
Required Inputs
- Cash today and monthly gross burn — the two non-negotiables.
- Monthly revenue and monthly revenue growth (optional — zero for pre-revenue).
- Volatility (optional, defaults: burn σ 10%, growth σ 25% of the growth rate) — from the requester's history if they have it, defaults if not, stated either way.
- Horizon (default 36 months) and simulation count (default 5,000).
Output Format
- The distribution — P10 (unlucky), P50 (median), P90 (lucky) runway in months, the survival probability at the horizon, and the naive cash÷net-burn number alongside for contrast.
- The death curve — % of simulated paths out of cash by each month; the months where it steepens are the danger window.
- The decision line — the one that matters: raise while P10 exceeds your fundraise time (6-9 months for most), not P50. Say explicitly when the P10 clock crosses that line.
- Stated model limits — normal noise (no fat tails), no seasonality, no fundraise events modelled. If their reality has lumpy enterprise revenue, say the P10 is optimistic.
Programmatic Helper
This skill ships scripts/runway_sim.py — zero dependencies, deterministic with --seed:
python3 scripts/runway_sim.py run runway.xlsx --cash 2400000 --burn 210000 --burn-vol 0.12 \
--revenue 60000 --rev-growth 0.05 --rev-vol 0.3
It prints the percentiles (naive=16.0mo P10=19 P50=>36 P90=>36 survive(36mo)=56.8%) and writes an .xlsx with an Assumptions sheet (editable cash/burn/revenue cells, live naive-runway formula) and a Death curve sheet. 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 · 92 tokens per session scan A 4339c111d95b
runway-monte-carlo is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 890 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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