# 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.

- **Type:** Skill
- **Install:** `agentstack add skill-wanshuiyin-auto-claude-code-research-in-sleep-analyze-results`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [wanshuiyin](https://agentstack.voostack.com/s/wanshuiyin)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [wanshuiyin](https://github.com/wanshuiyin)
- **Source:** https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/analyze-results

## Install

```sh
agentstack add skill-wanshuiyin-auto-claude-code-research-in-sleep-analyze-results
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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)

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [wanshuiyin](https://github.com/wanshuiyin)
- **Source:** [wanshuiyin/Auto-claude-code-research-in-sleep](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-wanshuiyin-auto-claude-code-research-in-sleep-analyze-results
- Seller: https://agentstack.voostack.com/s/wanshuiyin
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.