results-analysis

# Results Analysis Run **strict, evidence-first experimental analysis** for ML/AI research. Use this skill to produce a **strict analysis bundle**: - `analysis-report.md` - `stats-appendix.md` - `figure-catalog.md` - `figures/` Do **not** use this skill to draft a paper `Results` section or a full experiment wrap-up report. Those belong to `ml-paper-writing` or `results-report`. ## Core contract ### This skill is responsible for - validating experiment artifacts and comparison units, - running rigorous descriptive and inferential statistics, - generating **real scientific figures** when data/logs are available, - writing figure purposes, caption requirements, and interpretation checklists, - surfacing limits, blockers, and missing evidence explicitly. ### This skill is not responsible for - paper-ready `Results` prose, - manuscript narrative polishing, - project-level experiment retrospectives. If the user wants the complete post-experiment summary report, hand off to `results-report` after this bundle is ready. ## Non-negotiable quality bar 1. **Prefer real figures over figure specs.** If the data can be read, generate real figures. Do not stop at “recommended visualization”. 2. **Never fabricate statistics.** If sample size, seeds, or raw metrics are missing, state the blocker clearly. 3. **Report complete statistics.** Do not report only best scores or only p-values. 4. **Interpret every main figure.** Every major figure must have purpose, caption requ...