extract-parameters-from-digest

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.