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.

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 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 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 Upstream: [research planning](../research planning/), [idea generation](../idea generation/) Downstream: [experiment code](../experiment code/), [data analysis](../data analysis/) See also: [paper assembly](../paper assembly/)

experiment-design