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extract-parameters-from-digest

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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

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/PlanExeOrg/PlanExe/blob/HEAD/experiments/napkin_math/.claude/skills/extract-parameters-from-digest/SKILL.md

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Extract Parameters from a PlanExe Extraction-Input Digest

Overview

A drop-in alternative to extract-parameters-from-full that reads the digest produced by prepare_extract_input.py (see experiments/napkin_math/prepare_extract_input.py) rather than the full PlanExe HTML report.

The digest is the 137-recommended extraction bundle in 137's order: Executive Summary, Project Plan, Selected Scenario, Assumptions, Review Plan, Premortem, Expert Criticism, Data Collection. Strategic Decisions is replaced by Selected Scenario per proposal 139.

It mixes two formats:

  • Compressed sections (Selected Scenario, Review Plan, Premortem, Expert Criticism) — produced by compress_report_section. Bullets carry inline epistemic tags of the form [<source_status> | e=N r=N | quote: verified|unverified].
  • Raw sections (Executive Summary, Project Plan, Assumptions, Data Collection) — passed through unchanged from the PlanExe source. No inline tags.

The system prompt at system-prompt.txt explains how to read both formats.

Output schema and hard limits are identical to extract-parameters-from-full, so the two skills can be compared head-to-head on the same plan.

When to Use

  • The user has run prepare_extract_input.py against a PlanExe sample and wants parameters extracted from the resulting digest
  • The user is comparing whether this pipeline produces better parameters than feeding the full HTML report

For plain PlanExe HTML/text reports, use extract-parameters-from-full instead.

Workflow

  1. Get the digest path. Usually experiments/napkin_math/output/<plan-name>/extract_parameters_input.md. If the user did not provide one, ask. Do not guess.
  2. Read system-prompt.txt (sibling of this SKILL.md). Treat it as the authoritative extraction instructions.
  3. Read the digest file. Mid-sized — much smaller than a raw PlanExe HTML report. Compressed sections (Selected Scenario, Review Plan, Premortem, Expert Criticism) carry inline tags; raw sections (Executive Summary, Project Plan, Assumptions, Data Collection) do not.
  4. Canonicalize across sections. The four compressed sections often surface the same real-world quantity under different phrasings ("minimum viable rental rate" / "off-peak hourly price" / "speculative high hourly rate" all name one rate). Merge near-duplicates into a single canonical snake_case id before writing the JSON. Prefer framings closest to a modelling primitive (rate, count, fraction, amount-per-period). Two ids for the same quantity will silently fragment downstream bounds and Monte Carlo correlations.
  5. Produce the JSON following the schema at the end of system-prompt.txt. For compressed sections, map the inline source_status tags to the JSON value_type field: [explicit] → explicit, [derived] → derived, [inferred] → inferred, [missing] items belong in missing_values_to_estimate, [stress_test] items are scenario-stress inputs (not baseline key_values). For raw sections, apply general parameter-extraction triage.
  6. Output destination. Default: print JSON to the chat. If the user asks for a file, write to the path they specify. Default suggestion: <digest-basename>.parameters.json next to the digest.

Hard Rules (re-stated for emphasis)

  • JSON only. No markdown fences, no prose, no explanation.
  • Use the digest's tags where they exist. For compressed sections, prefer [explicit] + quote: verified items for baseline key_values. Treat quote: unverified items with extra scepticism. [missing] items belong in missing_values_to_estimate. [stress_test] items are downside-scenario inputs, not plan facts.
  • For raw sections (Executive Summary, Project Plan, Assumptions, Data Collection), apply general triage: prefer numeric anchors, deadlines, denominators, and explicit gate criteria.
  • Canonicalize across sections. The compressor over-produces on purpose; collapsing cross-section near-duplicates into one canonical id is your job at this stage, not its job. Never preserve two ids for the same real-world quantity.
  • Percentages as fractions between 0 and 1 with unit: "fraction".
  • No invented ids in formula_hint — every variable must be declared in key_values, missing_values_to_estimate, or the object's own depends_on.
  • Every entry with a non-null formula_hint MUST also declare output_name (snake_case id of the computed value) and output_unit (e.g. "DKK", "people", "fraction"). Downstream consumers — generate-calculations, run-scenarios, monte-carlo — read these directly and do not parse formula_hint or pattern-match on tokens. The LLM is the single authority for both fields.

Reference

  • System prompt (authoritative): system-prompt.txt
  • Producer of the input digest: experiments/napkin_math/prepare_extract_input.py
  • Parallel skill for full HTML reports: ../extract-parameters-from-full/SKILL.md
  • Background: docs/proposals/137-section_filtering_for_parameter_extraction.md, docs/proposals/139-compress-for-monte-carlo.md