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SkillsMP has collected 11 skills from PlanExeOrg/PlanExe. Open a skill to review its source and details.
Showing 11 of 11 collected skills.
Use when the user wants to extract parameters from a PlanExe extraction-input digest (the markdown produced by experiments/napkin_math/prepare_extract_input.py — the 137-recommended section bundle, with the four "Keep or compress" sections compressed) instead…
Use when the user wants to extract parameters, modelling values, or key variables from a PlanExe report (HTML or text) for napkin math, triage, or Monte Carlo simulation
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
Use when the user wants to run the napkin-math pipeline end-to-end on a PlanExe report, or resume a partially populated output directory by filling in only the missing stages. Orchestrates digest preparation, parameter extraction, validation, bounds,…
Use after the napkin_math 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…
Use after the napkin_math 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…
Use when the user wants to turn a validated extract-parameters-from-full JSON into a Python module of deterministic functions implementing the formula_hint expressions for downstream scenario runs and Monte Carlo
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…
Use when the user wants to compute deterministic low/base/high scenario outputs for a PlanExe model — given an extract-parameters-from-full JSON, a generate-bounds JSON, and a generate-calculations Python module — producing a scenario result JSON with inputs,…
Use after any change under experiments/napkin_math/ 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…
OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker.