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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.

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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analyze-results

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Use analyze-results to analyze experiment results in [results files or directory], compare [models/configurations] on the primary metric, and return a raw comparison table, statistically supported findings, and next experiments.

Required inputs

  • [results files or directory]
  • [models/configurations]

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SKILL.md

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

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)