| 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
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
{
"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
