schedule-monte-carlo
Project completion as a distribution, not a date — Monte Carlo over the task graph. Use when a plan's finish date came from summing 'likely' estimates (it's wrong, mathematically), when leadership needs a commit date, or when you need to know which tasks actually control the timeline. Produces P10/P50/P90 completion, per-task criticality (how often each task sits on the critical path), and a real .xlsx — via the bundled zero-dependency simulator, deterministic with a seed.
npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 1. **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. 2. **The commitment guidance** — promise P50 internally, P90 externally; the space between is the honesty budget. Name the dates. 3. **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 cri
- Required Inputs
- Output Format
- Programmatic Helper
- Quality Checks
- Anti-Patterns
python3 scripts/schedule_sim.py run schedule.xlsx --tasks tasks.json --sims 5000
What does the schedule-monte-carlo skill do?
Project completion as a distribution, not a date — Monte Carlo over the task graph. Use when a plan's finish date came from summing 'likely' estimates (it's wrong, mathematically), when leadership needs a commit date, or when you need to know which tasks actually control the timeline. Produces P10/P50/P90 completion, per-task criticality (how often each task sits on the critical path), and a real .xlsx — via the bundled zero-dependency simulator, deterministic with a seed.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.