Agent skill · Workflow & Productivity

experiment-plan

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

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claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill experiment-plan --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Allowed tools: Bash(*)ReadWriteEditGrepGlobWebSearchWebFetch
Path: skills/ai-ml/experiment-plan/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Experiment Plan: Claim-Driven, Paper-Oriented Validation Refine and concretize: **$ARGUMENTS** ## Overview Use this skill after the method is stable enough that the next question becomes: **what exact experiments should we run, in what order, to defend the paper?** If the user wants the full chain in one request, prefer `/research-refine-pipeline`. The goal is not to generate a giant benchmark wishlist. The goal is to turn a proposal into a **claim -> evidence -> run order** roadmap that supports four things: 1. the method actually solves the anchored problem 2. the dominant contribution is real and focused 3. the method is elegant enough that extra complexity is unnecessary 4. any frontier-model-era component is genuinely useful, not decorative ## Constants - **OUTPUT_DIR = `refine-logs/`** — Default destination for experiment planning artifacts. - **MAX_PRIMARY_CLAIMS = 2** — Prefer one dominant claim plus one supporting claim. - **MAX_CORE_BLOCKS = 5** — Keep the must-run experimental story compact. - **MAX_BASELINE_FAMILIES = 3** — Prefer a few strong baselines over many weak ones. - **DEFAULT_SEEDS = 3** — Use 3 seeds when stochastic variance matters and budget allows. ## Wo

What's inside
Steps it walks through
  1. Overview
  2. Constants
  3. Workflow
  4. Phase 0: Load the Proposal Context
  5. Phase 1: Freeze the Paper Claims
  6. Phase 2: Build the Experimental Storyline
  7. Phase 3: Specify Each Experiment Block
  8. Phase 4: Turn the Plan Into an Execution Order
  9. Phase 5: Write the Outputs
  10. Output Protocols
  11. Key Rules
  12. Composing with Other Skills
Ships with 1 file
  • metadata.json
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About this skill
What does the experiment-plan skill do?

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill experiment-plan --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going