glebis/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.
npx skills add https://github.com/glebis/claude-skills --skill whitepaper-audit
Audit a markdown white paper in two lanes and produce one merged, prioritized report.
audience-fit/jargon-undefineddepends on it. Default: "technical practitioners, non-academic".
recommend (default) or fix (only on explicit request).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.
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".
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.
Write a markdown report: summary verdict, findings table (id, severity, confidence,
location, fix), then details. Recommend; do not edit.
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.
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).
scripts/check_doc.py — lane 1 (stdlib-only; --help for flags)references/checklist.md — operational criteria, both lanesreferences/audit-prompt.md — judge prompt templateevals/ — judge validation cases + pass criteriaDESIGN.md — architecture decisions (v0.2, Codex-audited)Take glebis/whitepaper-audit from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.