Skill Claude Code
Use when the user wants to extract parameters from a PlanExe extraction-input digest (the markdown produced by experiments/napkinmath/prepareextractinput.py — the 137-recommended section bundle, with the four "Keep or compress" sections compressed) instead of the full PlanExe HTML report.
Skill Claude Code
Use when the user wants to generate low/base/high assumption ranges (bounds) for missing or uncertain variables in a validated extract-parameters-from-full JSON, in preparation for deterministic scenarios or Monte Carlo.
Skill Claude Code
Use when the user wants to turn a validated extract-parameters-from-full JSON into a Python module of deterministic functions implementing the formulahint expressions for downstream scenario runs and Monte Carlo.
Skill Claude Code
Use when the user wants Monte Carlo simulation of a PlanExe model — sampling from bounds to produce output distributions (mean/std/percentiles), threshold pass probabilities, and Pearson-correlation sensitivity rankings — given an extract-parameters-from-full JSON, a generate-bounds JSON, a generate-calculations…
Skill Claude Code
Use after the napkinmath pipeline has produced parameters/bounds/scenarios/montecarlo JSON to generate a plan assessment (assessment.md) — a thin interpretation layer over the intermediary artifacts. Emits a JSON manifest, a provenance map, gate verdicts (Critical / Fragile / Marginal / Robust), failure drivers…
Skill Claude Code
Use after any change under experiments/napkinmath/ or to the upstream skill prompts that feed into it (extract-parameters-from-full, extract-parameters-from-digest, generate-bounds, generate-calculations, run-scenarios, monte-carlo). Runs the smoke-test suite and reports pass/fail. Invoke before declaring napkin-math…
Skill Claude Code
Use after the napkinmath pipeline has produced parameters.json (from extract-parameters-from-digest or extract-parameters-from-full) to validate it against the 16 structural checks the rest of the pipeline assumes. Writes validation.json next to parameters.json. Deterministic Python — no LLM call.
Skill Claude CodeCodex
OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker.
MCP server Claude CodeCodexCursor +2
MCP server for generating rough-draft project plans from natural-language prompts. Remote server at mcp.planexe.org.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: