Analyze Results
When you've got a folder full of experiment JSONs and CSVs scattered across results directories, this skill pulls them together into comparison tables with proper baselines and deltas. It handles the tedious parts: computing means and standard deviations across seeds, flagging outliers, identifying trends in hyperparameter sweeps. What I like is the structured insight format that forces you to go from observation to interpretation to next experiment, which keeps you from just staring at numbers. It'll also propose documentation updates when findings look significant. Basically saves you from writing the same pandas aggregation code every time you need to compare model runs.
- skills.sh installs
- 584
- GitHub stars
- 18.9K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add wanshuiyin/auto-claude-code-research-in-sleep --skill analyze-results --agent claude-codeInstalls into .claude/skills of the current project.
Files
Analyze Experiment Results
Analyze: $ARGUMENTS
Workflow
Step 1: Locate Results
Find all relevant JSON/CSV result files:
- Check
figures/,results/, or project-specific output directories - Parse JSON results into structured data
Step 2: Build Comparison Table
Organize results by:
- Independent variables: model type, hyperparameters, data config
- Dependent variables: primary metric (e.g., perplexity, accuracy, loss), secondary metrics
- Delta vs baseline: always compute relative improvement
Step 3: Statistical Analysis
- If multiple seeds: report mean +/- std, check reproducibility
- If sweeping a parameter: identify trends (monotonic, U-shaped, plateau)
- Flag outliers or suspicious results
Step 4: Generate Insights
For each finding, structure as:
- Observation: what the data shows (with numbers)
- Interpretation: why this might be happening
- Implication: what this means for the research question
- Next step: what experiment would test the interpretation
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- Repository
- github.com/wanshuiyin/auto-claude-code-research-in-sleep
- Category
- Data Science & ML
- First seen
- Jun 3, 2026
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