Experiment Design

---
name: experiment-design
description: 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.
argument-hint: [idea-or-plan]
---

# 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
- Downstream: [experiment-code](../experiment-code/), [data-analysis](../data-analysis/)
- See also: [paper-assembly](../paper-assembly/)