---
name: Analyze Results
slug: analyze-results
category: Data
description: "Analyze Results analyzes ML experiment results, computes statistics, and generates comparison tables and insights. Use when the user says \"analyze results\", \"compare\", or needs to interpret experimental data."
github: "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/analyze-results"
language: Python
stars: 14656
forks: 1291
install: "npx degit https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/analyze-results ~/.claude/skills/analyze-results"
installs_to: ~/.claude/skills/analyze-results
source_path: skills/analyze-results/SKILL.md
collection_size: 25
category_size: 668
collection_url: "https://dirskills.com/collections/wanshuiyin/Auto-claude-code-research-in-sleep"
added: 2026-08-14T07:13:00.485Z
last_synced: 2026-08-14T07:13:00.485Z
canonical_url: "https://dirskills.com/skills/analyze-results"
---

# Analyze Results

Analyze Results analyzes ML experiment results, computes statistics, and generates comparison tables and insights. Use when the user says "analyze results", "compare", or needs to interpret experimental data.

**Install:**

```bash
npx degit https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/analyze-results ~/.claude/skills/analyze-results
```

## README

# 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)