git:20260310.c66f86d to git:20260529.8d26958

---

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]

---

# 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)