# Experiment Design

> Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.

- **Type:** Skill
- **Install:** `agentstack add skill-aravindan20-claude-research-paper-os-experiment-design`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ARAVINDAN20](https://agentstack.voostack.com/s/aravindan20)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [ARAVINDAN20](https://github.com/ARAVINDAN20)
- **Source:** https://github.com/ARAVINDAN20/Claude-Research-Paper-OS/tree/main/.claude/skills/agent-research-skills/skills/experiment-design

## Install

```sh
agentstack add skill-aravindan20-claude-research-paper-os-experiment-design
```

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

## About

# Experiment Design

Design structured, progressive experiment plans for research papers.

## Input

- `$0` — Research idea, plan, or method description

## References

- 4-stage progressive experiment prompts: `~/.claude/skills/experiment-design/references/stage-prompts.md`

## Scripts

### Generate experiment design
```bash
python ~/.claude/skills/experiment-design/scripts/design_experiments.py --plan research_plan.json --output experiment_design.json
python ~/.claude/skills/experiment-design/scripts/design_experiments.py --method "contrastive learning" --task classification --format markdown
```

Generates baselines, ablation matrix, hyperparameter grid, metric selection. Stdlib-only.

## 4-Stage Progressive Framework (from AI-Scientist-v2)

### Stage 1: Initial Implementation
- Focus on getting a basic working implementation
- Use a simple dataset
- Aim for basic functional correctness
- Completion: at least one working (non-buggy) implementation

### Stage 2: Baseline Tuning
- Tune hyperparameters (learning rate, epochs, batch size)
- Do NOT change model architecture
- Test on at least TWO datasets
- Completion: stable training curves, improvement over Stage 1

### Stage 3: Creative Research
- Explore novel improvements and insights
- Be creative and think outside the box
- Test on at least THREE datasets
- Completion: demonstrated novel improvement

### Stage 4: Ablation Studies
- Systematic component analysis
- Each ablation tests a different aspect
- Use same datasets as Stage 3
- Completion: all planned ablations done

## Output Format

```json
{
  "stages": [
    {
      "name": "initial_implementation",
      "goals": ["Basic working baseline", "Simple dataset"],
      "max_iterations": 5,
      "completion_criteria": "Working implementation with non-zero accuracy"
    }
  ],
  "baselines": ["Method A", "Method B"],
  "datasets": ["Dataset1", "Dataset2", "Dataset3"],
  "metrics": ["accuracy", "F1", "inference_time"],
  "ablation_components": ["component_A", "component_B"],
  "hyperparameter_grid": {
    "lr": [1e-4, 1e-3, 1e-2],
    "batch_size": [32, 64, 128]
  },
  "num_seeds": 3
}
```

## Rules

- Always start simple (Stage 1) before complex experiments
- Each stage builds on the best result from the previous stage
- Multi-seed evaluation for statistical significance
- Document every experiment run in notes.txt
- Generate figures for training curves and comparisons

## Related Skills
- Upstream: [research-planning](../research-planning/), [idea-generation](../idea-generation/)
- Downstream: [experiment-code](../experiment-code/), [data-analysis](../data-analysis/)
- See also: [paper-assembly](../paper-assembly/)

## Source & license

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

- **Author:** [ARAVINDAN20](https://github.com/ARAVINDAN20)
- **Source:** [ARAVINDAN20/Claude-Research-Paper-OS](https://github.com/ARAVINDAN20/Claude-Research-Paper-OS)
- **License:** Apache-2.0

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-aravindan20-claude-research-paper-os-experiment-design
- Seller: https://agentstack.voostack.com/s/aravindan20
- 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%.