| name | experiment-code |
| description | Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper. |
| argument-hint | ["plan-or-idea"] |
Experiment Code
Generate and iteratively improve ML experiment code for research papers.
Input
$0 — Task: generate, improve, debug, plot$1 — Research plan, idea description, or error message
References
- Experiment prompts and patterns:
~/.claude/skills/experiment-code/references/experiment-prompts.md
- Code patterns (error handling, repair, hill-climbing):
~/.claude/skills/experiment-code/references/code-patterns.md
Action: generate
Generate initial experiment code following this structure:
- Plan experiments first — List all runs needed (hyperparameter sweeps, ablations, baselines)
- Write self-contained code — All code in project directory, no external imports from reference repos
- Include proper logging — Save results to JSON, print intermediate metrics
- Generate figures — At minimum Figure_1.png and Figure_2.png
Mandatory Structure
project/
├── experiment.py # Main experiment script
├── plot.py # Visualization script
├── notes.txt # Experiment descriptions and results
├── run_1/ # Results from run 1
│ └── final_info.json
├── run_2/
└── ...
Constraints
- No placeholder code (
pass, ..., raise NotImplementedError)
- Must use actual datasets (not toy data unless explicitly requested)
- PyTorch or scikit-learn preferred (no TensorFlow/Keras)
- Each run uses:
python experiment.py --out_dir=run_i
Action: improve
Improve existing experiment code:
- Read current code and results
- Reflect on what worked and what didn't
- Apply targeted edits (prefer small edits over full rewrites)
- Re-run and compare scores
- Keep the best-performing code variant
Action: debug
Fix experiment code errors:
- Read the error message (truncate to last 1500 chars if very long)
- Identify the root cause
- Apply minimal fix
- Up to 4 retry attempts before changing approach
Action: plot
Generate publication-quality plots from experiment results:
- Read all
run_*/final_info.json files
- Generate comparison plots with proper labels
- Use the figure-generation skill for styling
Rules
- Always plan experiments before writing code
- After each run, document results in notes.txt
- Include print statements explaining what results show
- Method MUST not get 0% accuracy — verify accuracy calculations
- Use seeds for reproducibility
- Before each experiment include a print statement explaining exactly what the results are meant to show
Related Skills
