/results-analysis

name: results-analysis
description: This skill should be used when the user asks to "analyze experimental results", "run strict statistical analysis", "compare model performance", "generate scientific figures", "check significance", "do ablation analysis", or mentions interpreting experiment data with rigorous statistics and visualization. It focuses on strict analysis bundles, not Results-section prose.
tags: [Research, Analysis, Statistics, Visualization, Scientific Reporting]
version: 0.2.0

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/`

When the user asks for review, audit, no-write, dry-run, or when inputs are incomplete, use **read-only audit mode** instead of producing files or figures. In that mode, output only valid/invalid statistics, blockers, claim candidates, and what evidence is missing. If invoked by `/analyze-results`, the command layer may write a blocker summary, but this skill should not create figures, reports, or polished conclusions from incomplete evidence.

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,
• paper-ready figure/table packaging with `pubfig` / `pubtab`,
• project-level experiment retrospectives.

If the user wants the complete post-experiment summary report, hand off to `results-report` after this bundle is ready. If the user wants publication-grade figures/tables, export parameters, publication QA, or figure/table redesign, hand off to `publication-chart-skill`.

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”. Exception: in read-only audit mode, do not generate figures; describe what figure would be valid after evidence is complete. 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 requirements, and post-figure interpretation notes. 5. **Separate evidence from prose.** This skill produces analysis artifacts; it does not write manuscript sections.

Standard workflow

1. Inventory and validate artifacts

Start by identifying:

• metric tables (`csv`, `json`, `tsv`, logs),
• training curves and checkpoints,
• seeds / repeated runs,
• baselines, ablations, and comparison families,
• evaluation protocol metadata.

Validate:

• metric direction (higher/lower is better),
• unit of analysis (run, subject, fold, dataset, seed),
• number of runs / seeds,
• missing values or silent failures,
• comparability across methods.

If the comparison is not statistically valid, say so before continuing. Do not treat repeated `subject × task` rows, folds, windows, trials, or seeds as independent units unless the design justifies it. Common blocker: a `subject × task` summary table is usually a repeated-measure summary, not an independent subject-level sample. If subjects have multiple task rows or missing task cells, state that before any significance or winner claim.

2. Lock the comparison questions

Before running statistics, define the exact comparison questions:

• Which method is compared to which baseline?
• What is the primary metric?
• What is the repeated-measure unit?
• Which ablation or robustness questions matter?
• Which findings are decision-changing?

Do not mix unrelated comparisons into one undifferentiated table.

3. Run strict statistics

Always produce:

• descriptive statistics: `mean ± std` when appropriate,
• `95% CI` or another clearly justified interval,
• run/seed counts,
• significance tests with assumptions stated,
• effect sizes,
• multiple-comparison handling when several contrasts are reported.

Default expectation:

• check parametric assumptions first,
• use non-parametric fallback when assumptions fail,
• state exactly what was tested and on what samples.

See:

• `references/statistical-methods.md`
• `references/statistical-reporting.md`

4. Generate real scientific figures

Produce actual figures whenever artifacts are available.

Minimum expectation for a non-trivial analysis bundle:

• **one main comparison figure**,
• **one supporting figure** (training dynamics / ablation / breakdown / error analysis),
• **one exact numeric summary table** in markdown.

Every main figure must define:

• figure purpose,
• plotted variables,
• error bar meaning,
• caption requirements,
• interpretation checklist.

See:

• `references/visualization-best-practices.md`
• `references/figure-interpretation.md`

5. Write analysis artifacts

`analysis-report.md`

Summarize:

• the analysis question,
• key findings,
• strongest supported comparisons,
• main caveats,
• what changed in the experimental understanding,
• claim candidates that may later be used in reports or manuscript writing.

Each claim candidate should use this shape:

## Claim Candidates

- Claim:
  - Source evidence:
  - Allowed wording:
  - Forbidden stronger wording:
  - Uncertainty:
  - Next check:
  - Decision: keep | weaken | revise | discard

`stats-appendix.md`

Record:

• descriptive statistics,
• test choices,
• assumptio