---

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. argument-hint: "[results-path-or-description]" allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit

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)