whitepaper-audit

name

whitepaper-audit

description

Audit a white paper or long-form technical document against a research-grounded best-practices checklist. Two lanes — deterministic script checks (readability, undefined acronyms, structure blocks, broken links) plus an LLM-judge review (overclaims, inconsistent numbers, buried lede, limitations honesty, audience fit). Produces a prioritized P0–P2 findings report by default; applies fixes on explicit request (TDD for any code changes). Use when the user says "audit this white paper", "review my paper against best practices", "check this doc for overclaims", "whitepaper QA", "is this paper ready to publish", or wants prioritized recommendations on a technical document.

whitepaper-audit

Audit a markdown white paper in two lanes and produce one merged, prioritized report.

Inputs

• Document path (required) — markdown source, not PDF.
• Stated audience (ask if not given) — severity of audience-fit/jargon-undefined depends on it. Default: "technical practitioners, non-academic".
• Mode — recommend (default) or fix (only on explicit request).

Workflow

1. Lane 1 — deterministic

python3 scripts/check_doc.py <doc.md> --offline [--target-grade N] [--allow ACRO]

Drop --offline to also check http(s) links (HEAD→GET, timeouts; only broken is a finding). Output: JSON findings, schema in DESIGN.md.

2. Lane 2 — LLM judge

Dispatch a subagent (fresh context — never judge a document you wrote in the same context) with references/audit-prompt.md, filling {PATH} and {AUDIENCE}, plus the [judge] criteria from references/checklist.md. The judge returns JSON findings.

Judge calibration rules are binding: verbatim quotes required; no P0 at low confidence; "needs verification", never "factually wrong".