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)Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
Run a pre-mortem risk analysis on a PRD or launch plan. Categorizes risks as Tigers (real problems), Paper Tigers (overblown concerns), and Elephants (unspoken worries), then classifies as launch-blocking, fast-follow, or track. Use when preparing for launch, stress-testing a product plan, or identifying what could go wrong.
> Jurisdiction-aware wage/hour and employment Q&A — classification, overtime, meal/rest breaks, leave, final pay — answered for the specific state/country with the controlling rule researched and cited rather than stated from memory. Use when the user asks any employment law question, or says "what's the rule in [state]", "is this exempt", "do we have to pay overtime for", or "can we classify this as".
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.