experiment-plan

Convert research proposals into claim-driven experiment roadmaps with run orders and budgets.

2|Updated Mar 29, 2026
One-click install
npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill experiment-plan-satsuki-64
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: experiment-plan
Source: https://github.com/satsuki-64/MiniAgentWorkflow/tree/main/.skills/experiment-plan
Command: npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill experiment-plan-satsuki-64

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps researchers turn a refined proposal into a concrete, claim-driven experiment roadmap that guides validation and publication.

Core Features & Use Cases

  • Generate a compact, evidence-backed run order and budget plan from a research proposal.
  • Outline dominant and auxiliary claims, baselines, metrics, and failure analyses to defend the paper story.
  • Apply across ML, diffusion, RL, or traditional experiments, from pilot studies to full-scale validations.

Quick Start

Provide a refined research proposal and ask it to convert into an experiment roadmap.

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 executable experiment plan?

An experiment plan converts a refined research proposal into a claim-driven roadmap, specifying executable blocks, run order, budgets, baselines, and evaluation protocols to guide validation.

How do I structure run order and data budgets for machine learning experiments?

Structure run order and data budgets by outlining dominant and auxiliary claims, mapping run-level details, and allocating compute resources against defined metrics and success criteria for ML validation.

What do I need to prepare before generating an experiment roadmap?

You need a refined research proposal detailing your core claims and intended baselines to translate it into a concrete, actionable experiment roadmap with risk mitigations and failure analyses.

How do I define success criteria and failure analyses for a paper validation experiment?

Define success criteria by outlining dominant and auxiliary claims alongside baselines and metrics, then generate failure analyses to defend your paper story against potential experimental risks.

What is the best way to plan experiments for diffusion or reinforcement learning models?

The best way is using a claim-driven experiment roadmap that translates your research proposal into run-level details, compute budgets, and evaluation protocols tailored for diffusion or RL experiments.