How do I install the Whitepaper Audit skill?

Run npx skillmds@latest add glebis/whitepaper-audit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Whitepaper Audit safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Whitepaper Audit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Whitepaper Audit free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Whitepaper Audit?

glebis (@glebis) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Whitepaper Audit by glebis — Agent Skill

Name: Whitepaper Audit
Rating: 4.2 (5 reviews)
Author: 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.

glebis 3e18f8e 14 files · 50.4 KB Updated Sep 9, 2026

File contents

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)

---name: whitepaper-auditdescription: 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 ```bash 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)

Frequently asked questions