experiment-plan

Transform research proposals into claim-driven experiment roadmaps with ablation matrices and baseline comparisons.

1|Updated Jul 21, 2026
One-click install
npx skills add https://github.com/dogekiki/SP-test --skill experiment-plan-dogekiki
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-plan
Source: https://github.com/dogekiki/SP-test/tree/main/.trae/skills/experiment-plan
Command: npx skills add https://github.com/dogekiki/SP-test --skill experiment-plan-dogekiki

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the ambiguity of research planning by transforming abstract method proposals into concrete, claim-driven experiment roadmaps that are ready for academic or technical validation.

Core Features & Use Cases

  • Claim-Driven Planning: Maps research claims to specific, defensible experiment blocks to ensure every run serves a purpose.
  • Execution Roadmap: Generates a structured run order, compute budget, and milestone tracker to optimize GPU usage and time.
  • Use Case: Use this after refining a new machine learning method to generate a complete validation plan, including ablation studies, baseline comparisons, and failure analysis, ensuring the paper is ready for submission.

Quick Start

Use the experiment-plan skill to generate a detailed validation roadmap for the current research proposal.

Frequently Asked Questions about experiment-plan

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

FAQPage Schema
How do I create an experiment roadmap for machine learning research?▼

Create an experiment roadmap by transforming research proposals into claim-driven execution plans with ablation matrices, baseline comparisons, and compute-efficient validation protocols. This method ensures every run serves a specific validation purpose for academic submission.

What is claim-driven experiment planning?▼

Claim-driven experiment planning maps research claims to defensible experiment blocks. It ensures every run serves a purpose by structuring validation protocols around specific technical contributions and ablation studies.

How do I design an ablation matrix and baseline comparisons for ML validation?▼

Design an ablation matrix and baseline comparisons by mapping research claims to specific experiment blocks. This approach structures validation protocols to justify technical contributions and ensure defensible results.

Can I generate a compute budget and milestone tracker for academic validation?▼

Generate a compute budget and milestone tracker by building a structured run order for machine learning research. This optimizes GPU usage and time management while preparing the paper for submission.

Do I need existing refinement logs to plan a validation protocol?▼

Existing refinement logs are required to prioritize essential experiments and justify technical contributions. This context ensures the generated validation protocol focuses on the most impactful claims and runs.

When should I use a structured experiment roadmap over ad-hoc ML testing?▼

Use a structured experiment roadmap instead of ad-hoc testing when preparing a machine learning method for academic submission. It solves research planning ambiguity by ensuring every run validates a specific claim.