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.

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 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: 1. Read current code and results 2. Reflect on what worked and what didn't 3. Apply targeted edits (prefer small edits over full rewrites) 4. Re run and compare scores 5. Keep the best performing code variant Action: debug Fix experiment code errors: 1. Read the error message (truncate to last 1500 chars if very long) 2. Identify the root cause 3. Apply minimal fix 4. Up to 4 retry attempts before changing approach Action: plot Generate publication quality plots from experiment results: 1. Read all run /final info.json files 2. Generate comparison plots with proper labels 3. 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 Upstream: [experiment design](../experiment design/), [algorithm design](../algorithm design/) Downstream: [data analysis](../data analysis/), [backward traceability](../backward traceability/) See also: [code debugging](../code debugging/), [paper to code](../paper to code/)

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