experiment-plan

Generates claim-driven experiment roadmaps with run order, baselines, and compute budgets for research papers.

Updated Jul 9, 2026
One-click install
npx skills add https://github.com/Lingjie-wang/autoRL --skill experiment-plan-lingjie-wang
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: experiment-plan
Source: https://github.com/Lingjie-wang/autoRL/tree/main/Auto-claude-code-research-in-sleep/skills/experiment-plan
Command: npx skills add https://github.com/Lingjie-wang/autoRL --skill experiment-plan-lingjie-wang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Turning a refined research proposal into a concrete, paper-defensible experiment plan is hard: researchers often over-plan benchmarks, miss ablations that isolate novelty, or fail to justify LLM/VLM/Diffusion/RL components. This Skill converts a proposal into a structured claim-to-evidence roadmap with prioritized run order. ## Core Features & Use Cases - Claim Mapping: Freezes primary and supporting claims, anti-claims, and the minimum convincing evidence each reviewer would need. - Experiment Block Design: Specifies datasets, baselines, metrics, success criteria, and failure interpretations for each block, capped at a compact paper storyline. - Execution Planning: Produces milestone-based run order with compute cost, decision gates, risks, and a must-run vs nice-to-have split, written to EXPERIMENT_PLAN.md and EXPERIMENT_TRACKER.md. - Use Case: After refining a method with research-refine, ask for an experiment plan to get an ablation matrix, baseline families, and the first three runs to launch. ## Quick Start Ask the assistant to create a detailed experiment plan for your refined research proposal, including ablations, baselines, run order, and compute budget.

Frequently Asked Questions about experiment-plan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn a research proposal into an experiment plan?

Provide your refined proposal or method idea and request an experiment plan. The Skill freezes paper claims, designs experiment blocks with baselines and metrics, and outputs a run order with milestones, decision gates, and compute estimates.

How to design ablation studies for a machine learning paper?

The Skill builds ablations around claim isolation: a novelty block tests the dominant contribution, a simplicity block compares against an overbuilt variant, and a frontier necessity block justifies any LLM, VLM, Diffusion, or RL component against simpler alternatives.

What inputs does the experiment plan skill need?

It reads refine-logs/FINAL_PROPOSAL.md, REVIEW_SUMMARY.md, and REFINEMENT_REPORT.md if they exist, extracting the problem anchor, contributions, reviewer concerns, and constraints. Without those files, it derives the same context from your prompt.

Can I use this without running research-refine first?

Yes. It works standalone whenever you ask for an experiment plan, ablation matrix, or evaluation protocol, though it is designed to follow research-refine. For the full chain in one request, use research-refine-pipeline.

What are the limitations of this experiment planning approach?

It plans evidence but does not execute runs or fabricate results; use run-experiment for execution. It also caps scope deliberately, so it may cut interesting experiments that do not defend a core paper claim.