raja21068/ai-paper-generator/experimental-design

Claude Code and agent skill by raja21068/ai-paper-generator

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Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

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npx vskill@latest install raja21068/ai-paper-generator/experimental-design

SKILL.md

The same SKILL.md was published earlier as aiming-lab/autoresearchclaw/experimental-design.

Experimental Design Best Practice

  1. ALWAYS include meaningful baselines (not just random):
  • At least one classical method baseline
  • At least one recent SOTA method baseline
  • A simple-but-strong baseline (e.g., linear probe, k-NN)
  1. Use MULTIPLE random seeds (minimum 3, ideally 5)
  2. Report mean +/- std across seeds
  3. Design ablations that isolate EACH key component:
  • Remove one component at a time
  • Each ablation must be meaningfully different from baseline
  1. Control variables: change only ONE thing per comparison
  2. Use standard splits (train/val/test) — never test on training data
  3. Report wall-clock time and memory usage alongside accuracy

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