paper-claim-audit
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says \"审查论文数据\", \"check paper claims\", \"verify numbers\", \"论文数字核对\", or before submission to ensure paper-to-evidence fidelity.
npx skills add majiayu000/claude-skill-registry --skill paper-claim-audit-wanshuiyin-auto-claude-code-res --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Paper Claim Audit: Zero-Context Evidence Verification Verify that every claim in the paper matches raw evidence for: **$ARGUMENTS** ## Why This Exists The executor writes experiments AND writes the paper. It "knows" what the results should be. This creates confirmation bias: - Rounding 84.7% up to 85.3% - Reporting best seed instead of average - Citing metrics from a different experiment config - Claiming "improves by 15%" when the delta is actually 12.8% A **fresh reviewer with zero prior context** catches these because it has no expectations — it just compares paper text vs raw files. ## How This Differs From Other Audit Skills | Skill | Question it answers | |-------|-------------------| | `/experiment-audit` | Is the experiment code honest? (fake GT, normalization fraud) | | `/result-to-claim` | Does the data scientifically support this claim? | | **`/paper-claim-audit`** | **Does the paper report the data truthfully and precisely?** | ## Core Principle **Zero-context, fresh reviewer.** The auditor receives ONLY: - Paper .tex files (the claims) - Raw result files (the evidence) It does NOT receive: - ❌ EXPERIMENT_LOG.md - ❌ EXPERIMENT_TRACKER.md - ❌ AUTO_REVIEW.md - ❌ NARRATI
- Why This Exists
- How This Differs From Other Audit Skills
- Core Principle
- Workflow
- Step 1: Collect Files (Executor — Claude)
- Step 2: Fresh Reviewer Audit (GPT-5.4 — NEW thread, no reply)
- Step 3: Write Report (Executor — Claude)
- Step 4: Print Summary
- When to Run
- Integration with Other Skills
- Read by /auto-paper-improvement-loop (if exists)
- Advisory, Never Blocking
- Render HTML view (auto, when RENDERHTML = true, default)
- Key Rules
What does the paper-claim-audit skill do?
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says \"审查论文数据\", \"check paper claims\", \"verify numbers\", \"论文数字核对\", or before submission to ensure paper-to-evidence fidelity.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill paper-claim-audit-wanshuiyin-auto-claude-code-res --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.