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Zero-context verification that every numeric claim in a paper matches raw result files; detects rounding, cherry-picking, config mismatches and scope overclaims
A zero-context auditor that compares quantitative claims in LaTeX paper sources against raw experiment result files to verify numbers, percentages, and comparative statements. It enforces strict rules (no executor summaries, standard rounding only) and produces machine-readable audit artifacts (JSON + Markdown) with detailed per-claim status.
Run before submission or after automated paper-generation steps when you need a forensic check that the paper's reported numbers are faithful to raw outputs. Useful as a pre-flight for reproducibility, CI checks on paper pipelines, or editorial review.
Best fit for Claude Code / Codex-style agents and any agent orchestration that can spawn a fresh reasoning thread and read files. The skill describes using strong reasoning models (e.g., GPT-5.x-class) as a separate reviewer thread.
Paper claim audit skill for verifying numeric claims in academic papers against raw result files. SKILL.md-only skill with no executable scripts. Exceptionally detailed and well-structured documentation covering a comprehensive audit protocol with specific failure modes, cross-model reviewer pattern, and JSON schema output contract. Targets a niche but real need for ML researchers preparing paper submissions. No security concerns — uses allowed-tools broadly but appropriately for a file auditing task. Source path in DB pointed to wrong directory (other/ vs analysis/), and raw content fetch returned 404, suggesting the skill moved or was reorganized.
SKILL.md body was null in fetch output and GitHub raw URL returned 404 at the stored path. Content was located via GitHub API at skills/analysis/ instead of skills/other/. The skill is well-crafted with detailed audit protocol, zero-context reviewer pattern, and proper verdict decision table. Uses allowed-tools: Bash(*) which is broad but appropriate for the task scope.
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