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experiment-design

根据研究想法或研究计划，按四阶段递进框架生成实验设计：初始实现、基线调参、创新研究、消融实验，并输出基线、数据集、指标、超参数网格与消融矩阵；附带仅使用 Python 标准库的生成脚本。

lingzhi227
热度 5.86
最近更新 2026-10-20 17:30:43
分类：知识依赖：代码执行 / 读写文件应用场景：研究 / 任务管理

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add lingzhi227/agent-research-skills/experiment-design
Metadata
name
experiment-design
description
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.
argument-hint
["idea-or-plan"]

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

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 as Stage 3
  • Completion: all planned ablations done

Output Format

{
  "stages": [
    {
      "name": "initial_implementation",
      "goals": ["Basic working baseline", "Simple dataset"],
      "max_iterations": 5,
      "completion_criteria": "Working implementation with non-zero accuracy"
    }
  ],
  "baselines": ["Method A", "Method B"],
  "datasets": ["Dataset1", "Dataset2", "Dataset3"],
  "metrics": ["accuracy", "F1", "inference_time"],
  "ablation_components": ["component_A", "component_B"],
  "hyperparameter_grid": {
    "lr": [1e-4, 1e-3, 1e-2],
    "batch_size": [32, 64, 128]
  },
  "num_seeds": 3
}

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

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