experiment-audit

name	experiment-audit
description	Audit experiments for top ML, CV, systems, and architecture submissions, checking baselines, ablations, metrics, statistical strength, datasets, stress tests, failure cases, and claim coverage.
version	0.1.0

Experiment Audit

Use this skill before submission when the evidence might be thin.

Workflow

1. Extract each major paper claim.
2. Map claims to experiments and identify missing support.
3. Check baselines, ablations, metrics, seeds, splits, compute, runtime, qualitative examples, and failure cases.
4. Prioritize new experiments by reviewer impact and time cost.

Output

Return Claim-evidence table, Missing baselines, Ablation gaps, High-impact experiments, and Reviewer risk.