experiment-codelisted

# 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: 1. **Plan experiments first** — List all runs needed (hyperparameter sweeps, ablations, baselines) 2. **Write self-contained code** — All code in project directory, no external imports from reference repos 3. **Include proper logging** — Save results to JSON, print intermediate metrics 4. **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