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.
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.
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.
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
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.
Never fabricate statistics.
If sample size, seeds, or raw metrics are missing, state the blocker clearly.
Report complete statistics.
Do not report only best scores or only p-values.
Interpret every main figure.
Every major figure must have purpose, caption requirements, and post-figure interpretation notes.
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:
For every major figure, answer all three questions:
Why does this figure exist?
What exactly should the reader notice?
What does that observation change in our belief or next decision?
If a figure cannot answer question 3, it is probably decorative rather than scientific.
Read-only audit mode
Use this mode when:
the user asks to audit or review existing artifacts,
the environment is read-only,
the user forbids file writes or figure generation,
core evidence is missing.
Return:
analysis questions,
valid statistics,
invalid or unsafe statistics,
claim candidates with allowed and forbidden wording,
blockers before report/figure generation.
Do not create analysis-output/, figures, or reports in this mode.
Quarantine any statistics file whose interpretation contradicts its own p-value, test method, unit of analysis, or comparison family. Do not reuse that file for claim wording until provenance is checked.
Failure mode policy
When inputs are incomplete, say so explicitly.
Examples:
no seed-level data -> descriptive summary only; inferential claims blocked,
no comparable baseline outputs -> no significance claim,
no readable logs -> cannot generate dynamics figure,
too few runs -> effect size may be unstable; report this limitation.
unclear unit of analysis -> no winner claim or significance claim,
analysis file with contradictory interpretation -> quarantine it until provenance is checked.
Never replace missing evidence with confident prose.
Reference files
Load only what is needed:
references/statistical-methods.md - test selection and assumptions
references/statistical-reporting.md - minimum reporting standard
