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/schedule-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/schedule-monte-carlo)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/schedule-monte-carlo"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/schedule-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/schedule-monte-carlo"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/schedule-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 | $0.00103 | $0.00811 |
| Opus 5 | $0.00051 | $0.00405 |
| Sonnet 5 | $0.00021 | $0.00162 |
| Haiku 4.5 | $0.00010 | $0.00081 |
Grade A, and why
schedule-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 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schedule Monte Carlo
Summing the "likely" estimates systematically understates the finish: parallel branches mean the slowest path wins each roll, and that maximum is always worse than the middle. This skill runs the actual simulation — thousands of schedule rolls over the dependency graph — and reports the date the way it behaves: as percentiles.
Required Inputs
- The task list with three-point estimates — per task: optimistic / likely / pessimistic (any consistent unit) and dependencies. Honest pessimistics are the whole game: "what if the API vendor ghosts us for two weeks" belongs in that number.
- Simulation count and seed (optional; defaults 5,000 and a fixed seed — results are reproducible).
Output Format
- The headline gap — deterministic finish (sum-of-likelies) vs P50 vs P90, side by side. The deterministic-to-P50 gap is the lie the old plan told; show it first.
- The commitment guidance — promise P50 internally, P90 externally; the space between is the honesty budget. Name the dates.
- Criticality table — per task, the share of simulations where it sat on the critical path. The top 2-3 are where management attention belongs; a task at 0.9 criticality with a wide estimate range is the schedule.
- Model limits — no resource contention or calendar effects; real schedules are worse, so these are optimistic floors.
Programmatic Helper
Ships scripts/schedule_sim.py — zero dependencies, cycle-detecting, deterministic:
python3 scripts/schedule_sim.py run schedule.xlsx --tasks tasks.json --sims 5000
# tasks.json: [{"name":"design","optimistic":3,"likely":5,"pessimistic":10,"depends":[]}, …]
Prints deterministic=21.0 P10=22.3 P50=27.0 P90=32.3 · top critical: design, integrate… and writes the summary + criticality sheets. Requires a code-execution environment.
Quality Checks
- The simulation ran (output quoted); percentiles were never eyeballed
- The deterministic-vs-P50 gap is stated explicitly and first — it is the finding most rooms need
- Criticality is reported per task and drives the "watch these" recommendation
- Pessimistic estimates were interrogated: if every task's pessimistic is likely×1.2, say the inputs are optimistic theatre and the output inherits it
- Internal-vs-external commitment dates are both named
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 · 49 lines · 103 tokens per session scan A 434f53b3f6a4
schedule-monte-carlo is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 103 tokens to every session and 811 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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