experiment-designlisted

# 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