Skill Claude CodeCodex
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 CodeCodex
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.
Skill Claude CodeCodex
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 CodeCodex
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 CodeCodex
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 CodeCodex
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.…
Skill Claude CodeCodex
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, outputs, comparison spread, and warnings.
Skill Claude CodeCodex
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 CodeCodex
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 CodeCodex
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.