PlanExeOrg

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 of the full PlanExe HTML report

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, calculations, scenarios, Monte Carlo, and assessment rendering. Never copies artifacts forward from prior runs, and never re-runs a stage whose output is already on disk.

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 (Critical / Fragile / Marginal / Robust), failure drivers, confidence and trust boundaries, scenario sanity check, and suggested next actions. The artifact is a navigation/judgment file, not a copy of the raw simulation data.

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 parameters.json. Deterministic Python — no LLM call.

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 JSON, a generate-bounds JSON, a generate-calculations Python module, and optional run settings

Replace or improve an existing example plan on the PlanExe-web Jekyll site (planexe.org) while preserving its name, images, title, and description. Use this skill whenever the user wants to replace, improve, or update an existing plan's zip and report. Trigger on mentions of "replace plan", "improve plan", "update plan", "plan needs updating", "replace the report", or any reference to updating an existing plan on the planexe.org examples gallery.

Add a new example plan to the PlanExe-web Jekyll site (planexe.org). Use this skill whenever the user wants to add a new example plan to the examples gallery. Trigger on mentions of "add plan", "new example", "new plan", "examples.yml", "plan zip", "report HTML", "process the input", "process input", or any reference to adding to the planexe.org examples gallery — even if the user just says something like "I have a new plan to add" or "process the input".

Start, stop, and manage the local Jekyll development server for PlanExe-web. Use this skill when doing web development on the site — editing CSS, layouts, index.md, examples, or any site content that needs a local preview.