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PlanExeOrg

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• 11 skills
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• 9 hours ago last updated

1. ▌
   Validate Parameters · planexeorg

   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.
   0 installs
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   Summarize Assessment · planexeorg

   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.
   0 installs
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   Generate Calculations · planexeorg bundle

   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
   0 installs
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   Run Napkin Math Pipeline · planexeorg

   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.
   0 installs
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   Extract Parameters From Full · planexeorg bundle

   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
   0 installs
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   Extract Parameters From Digest · planexeorg bundle

   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
   0 installs
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   Planexe MCP · planexeorg

   OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker.
   0 installs
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   Monte Carlo · planexeorg

   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
   0 installs
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   Run Scenarios · planexeorg bundle

   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
   0 installs
10. ▌
    Generate Bounds · planexeorg bundle

    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
    0 installs
11. ▌
    Test Napkin Math · planexeorg

    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 suite and reports pass/fail. Invoke before declaring napkin-math work done.
    0 installs