Markitdown
ImCa0/just-laws
Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.
Project completion as a distribution, not a date — Monte Carlo over the task graph.
Install Schedule Monte Carlo
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carlo --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/schedule-monte-carlo .claude/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .claude/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carloType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carlo --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/schedule-monte-carlo .agents/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .agents/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carlo --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/schedule-monte-carlo .cursor/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .cursor/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mohitagw15856/pm-claude-skills.git --path skills/schedule-monte-carlo--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carlo --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/schedule-monte-carlo .gemini/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .gemini/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carloInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/schedule-monte-carlo .github/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .github/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills schedule-monte-carlo --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/schedule-monte-carlo .opencode/skills/schedule-monte-carlo && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "schedule-monte-carlo" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/schedule-monte-carlo into .opencode/skills/schedule-monte-carlo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schedule-monte-carlo", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
schedule-monte-carlo- GitHub stars
- 1.4k
- Token cost
- ~920 tokens
- SKILL.md length
- 420 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Project completion as a distribution, not a date — Monte Carlo over the task graph.
- Works in 4 steps: The headline gap — deterministic finish… → The commitment guidance — promise P50… → Criticality table — per task, the share… → …
- A plans finish date came from summing likely estimates (its wrong
- SKILL.md covers Required Inputs, Output Format, Programmatic Helper and Quality Checks, plus 2 more sections
- Runs Python scripts from its folder; calls python3
What it does
Schedule Monte Carlo is an agent skill from mohitagw15856/pm-claude-skills. 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.
Its SKILL.md is about 920 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/schedule_sim.py`).
It sits in Documents & Office, covering Excel spreadsheets. It works with Microsoft Excel. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- A plans finish date came from summing likely estimates (its wrong
- Mathematically)
- Leadership needs a commit date
- You need to know which tasks actually control the timeline
Example prompts
- “s finish date came from summing”
- “estimates (it”
- “/schedule-monte-carlo”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 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…
- 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…
- Model limits — no resource contention or calendar effects; real schedules are worse, so these are optimistic floors.
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Schedule Monte Carlo loads about 920 tokens when it runs. Until then it costs about 125 tokens; SKILL.md has 420 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 420 words, ~920 tokens.
Download SKILL.mdSave it as
.claude/skills/schedule-monte-carlo/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- schedule-monte-carlo
- description
- 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.
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.
Show full SKILL.md (185 more words)Show less
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
Anti-Patterns
- Do not present P50 as "the date" — the median loses half the time, by definition
- Do not let uniform ±20% estimates pass silently — real uncertainty is lumpy, and flat inputs mean nobody thought about failure modes
- Do not hide the deterministic number — showing plan-math next to real-math is how the method earns adoption
- Do not add hidden buffers on top of P90 — the whole point is replacing padding with arithmetic
- Do not simulate a 200-task plan at task granularity — roll up to workstreams; precision theatre at that scale is its own lie
Example Trigger Phrases
- "When will this project really finish?"
- "Leadership needs a commit date."
- "Simulate our schedule instead of summing estimates."
- "Which tasks actually drive the end date?"
© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (scripts) in skills/schedule-monte-carlo of mohitagw15856/pm-claude-skills.
- SKILL.md
- scripts/schedule_sim.py
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Schedule Monte Carlo next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Schedule Monte Carlo this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~920 | Automated safety check: Pass | MIT | |
| MarkitdownImCa0/just-laws | 781 | 14 repos | ~3.2k | Automated safety check: Notes | MIT | |
| Data Table Managern8n-io/n8n | 207k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Docx4jplutext/docx4j | 2.4k | — | ~2.5k | Automated safety check: Pass | None | |
| Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 2 repos | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Cyber Pptcrazyykhllc-bit/CyberPPT | 1.8k | — | ~10k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Schedule Monte Carlo
What does Schedule Monte Carlo do?
Project completion as a distribution, not a date — Monte Carlo over the task graph. Schedule Monte Carlo is an agent skill from mohitagw15856/pm-claude-skills. Project completion as a distribution, not a date — Monte Carlo over the task graph.
When should I use Schedule Monte Carlo?
Schedule Monte Carlo fits situations like: A plans finish date came from summing likely estimates (its wrong; mathematically); leadership needs a commit date; you need to know which tasks actually control the timeline.
How do I install Schedule Monte Carlo in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a claude-code`. Or copy the skill folder (skills/schedule-monte-carlo in mohitagw15856/pm-claude-skills) into .claude/skills/schedule-monte-carlo in your project. Claude Code loads it when a task matches its description.
How do I install Schedule Monte Carlo in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a codex`. Or copy the skill folder (skills/schedule-monte-carlo in mohitagw15856/pm-claude-skills) into .agents/skills/schedule-monte-carlo in your project. Codex loads it when a task matches its description.
Can I use Schedule Monte Carlo in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitagw15856/pm-claude-skills --skill schedule-monte-carlo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/schedule-monte-carlo, .gemini/skills/schedule-monte-carlo, .github/skills/schedule-monte-carlo and .opencode/skills/schedule-monte-carlo in your project.
What does Schedule Monte Carlo need to run?
Going by SKILL.md and its folder, Schedule Monte Carlo needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Schedule Monte Carlo access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Schedule Monte Carlo safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Schedule Monte Carlo use?
Schedule Monte Carlo is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Schedule Monte Carlo use?
About 920 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Schedule Monte Carlo?
Skills that share tags, products or a category with Schedule Monte Carlo: Markitdown (ImCa0/just-laws, 781 stars), Data Table Manager (n8n-io/n8n, 207k stars), Docx4j (plutext/docx4j, 2.4k stars) and Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Schedule Monte Carlo?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.