skills/33-Galaxy-Dawn-claude-scholar/skills/results-analysis/SKILL.md
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.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research results-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Run strict, evidence-first experimental analysis for ML/AI research.
Use this skill to produce a strict analysis bundle:
analysis-report.mdstats-appendix.mdfigure-catalog.mdfigures/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.
Results prose,If the user wants the complete post-experiment summary report, hand off to results-report after this bundle is ready.
Start by identifying:
csv, json, tsv, logs),Validate:
If the comparison is not statistically valid, say so before continuing.
Before running statistics, define the exact comparison questions:
Do not mix unrelated comparisons into one undifferentiated table.
Always produce:
mean ± std when appropriate,95% CI or another clearly justified interval,Default expectation:
See:
references/statistical-methods.mdreferences/statistical-reporting.mdProduce actual figures whenever artifacts are available.
Minimum expectation for a non-trivial analysis bundle:
Every main figure must define:
See:
references/visualization-best-practices.mdreferences/figure-interpretation.mdanalysis-report.mdSummarize:
stats-appendix.mdRecord:
figure-catalog.mdFor each figure, record:
Do not finish until all are true:
Results draft is included.analysis-output/
├── analysis-report.md
├── stats-appendix.md
├── figure-catalog.md
└── figures/
├── figure-01-main-comparison.pdf
├── figure-02-ablation.pdf
└── ...
For every major figure, answer all three questions:
If a figure cannot answer question 3, it is probably decorative rather than scientific.
When inputs are incomplete, say so explicitly.
Examples:
Never replace missing evidence with confident prose.
Load only what is needed:
references/statistical-methods.md - test selection and assumptionsreferences/statistical-reporting.md - minimum reporting standardreferences/visualization-best-practices.md - publication-quality figure rulesreferences/figure-interpretation.md - how to explain figures with evidencereferences/analysis-depth.md - move from observation to mechanism and decisionreferences/common-pitfalls.md - common analysis and reporting failuresexamples/example-analysis-report.mdexamples/example-stats-appendix.mdexamples/example-figure-catalog.mddevelopment
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.