AI & Automation Listed
experiment-audit
Use this skill for scientific and ML-research reasoning work — evaluating experimental claims, auditing training runs or ablations, checking whether a statistical claim holds up, assessing reproducibility, reconciling contradictory results, reviewing a paper's methodology or results section, writing reviewer-style feedback, or producing a structured research report. This is a scientific reasoning discipline, not just a tool wrapper, so it applies even with no live data source — e.g. reviewing a pasted table of results, sanity-checking a claimed effect size, or evaluating an ablation described in prose. Trigger on phrasing like "did I mess up this experiment," "is this result real," "why did my loss/reward do X," "which run is better," "is this ablation confounded," "review this paper's claims," "write reviewer feedback," "is this reproducible," "what should I conclude from this," or "write up these results." When the user's data lives in Weights & Biases, this skill also covers the experiment-audit-mcp integr
