analyze-results

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Translated

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.

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Sourcewanshuiyin/auto-claude-code-research-in-sleep
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NPX Install

npx skill4agent add wanshuiyin/auto-claude-code-research-in-sleep analyze-results

Tags

Translated version includes tags in frontmatter
ml-experiment-analysisstatistical-computationresult-comparisondata-insightsdocumentation-update

SKILL.md Content

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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)

Output Format

Always include:
  1. Raw data table
  2. Key findings (numbered, concise)
  3. Suggested next experiments (if any)