analyze-results
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
npx skills add majiayu000/claude-skill-registry --skill analyze-results --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: 1. **Observation**: what the data shows (with numbers) 2. **Interpretation**: why this might be happening 3. **Implication**: what this means for the research question 4. **Next step**: what experiment would test the interpretation ### Step 5: Update Documentation If findings are significant: - Propose updates to project notes or experiment reports - Draft a concise finding statement (1-2 sentences) #
- Workflow
- Step 1: Locate Results
- Step 2: Build Comparison Table
- Step 3: Statistical Analysis
- Step 4: Generate Insights
- Step 5: Update Documentation
- Output Format
What does the analyze-results skill do?
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill analyze-results --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.