Agent-Skills.md

Agent Skills: whitepaper-audit

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.

UncategorizedID: glebis/claude-skills/whitepaper-audit

Repository

glebis/claude-skills
glebisLicense: MIT
37956

Install this agent skill to your local

pnpm dlx add-skill https://github.com/glebis/claude-skills/tree/HEAD/whitepaper-audit

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whitepaper-audit/SKILL.md

Skill Metadata

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".

3. Merge

Dedupe by (location, issue type) keeping both lane attributions; sort P0 → P1 → P2, then confidence. Cross-reference: a lane-1 broken link that supports a claim (judge decides materiality) is P1; decorative → P2.

4. Report (default mode)

Write a markdown report: summary verdict, findings table (id, severity, confidence, location, fix), then details. Recommend; do not edit.

5. Fix mode (only when explicitly requested)

Apply fixes P0-first. Any change to code goes through superpowers test-driven-development (test first, watch it fail). Prose fixes: edit, then re-run the full audit and report cleared vs remaining findings.

Evals

Before trusting a new/changed judge prompt, run evals/README.md procedure (planted defects + clean control; pass criteria inside). Lane 1 is covered by scripts/tests/test_check_doc.py (pytest).

Files

  • scripts/check_doc.py — lane 1 (stdlib-only; --help for flags)
  • references/checklist.md — operational criteria, both lanes
  • references/audit-prompt.md — judge prompt template
  • evals/ — judge validation cases + pass criteria
  • DESIGN.md — architecture decisions (v0.2, Codex-audited)