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ablation-planner

Generate reviewer-aligned ablation study plans with coverage and feasibility assessments.

Updated May 20, 2026
Search Tags:#model evaluation#paper submission#ml experiments#ablation studies#reviewer evidence#hyperparameter sensitivity
One-click install
npx skills add https://github.com/lightrain-a/medtrace-aris --skill ablation-planner-lightrain-a
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ablation-planner
Source: https://github.com/lightrain-a/medtrace-aris/tree/main/.vendor/aris/skills/ablation-planner
Command: npx skills add https://github.com/lightrain-a/medtrace-aris --skill ablation-planner-lightrain-a

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ablation planning helps transform uncertain experimental progress into a clear, reviewer-aligned set of experiments that isolate each claimed contribution and validate key design choices.

Core Features & Use Cases

  • Reviewer-perspective ablation design: Creates component and hyperparameter ablations that answer the questions most reviewers will ask about contributions, sensitivity, and alternatives.
  • Feasibility-first implementation plan: Ensures ablations are implementable under compute and dependency constraints, with suggested cuts and run order.
  • Evidence-ready study coverage: Produces structured outputs including what each ablation tests, expected outcomes if the component matters, coverage assessment, unnecessary experiments to skip, and compute estimates.
  • Use Case: When your main results support the claims but you need ablation evidence for a paper submission, use this skill to generate a prioritized, executable ablation plan.

Quick Start

Ask the system to generate an ablation plan for your paper submission when your results show partial or full evidence for the result-to-claim output.

Frequently Asked Questions about ablation-planner

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

FAQPage Schema
How do I design ablation studies for paper submission that satisfy reviewer evidence requirements?▼

A reviewer-proof ablation plan isolates claimed contributions by testing component removals, replacements, and hyperparameter sensitivity, outputting structured evidence including expected outcomes, coverage assessments, and unnecessary experiments to skip.

What is the best way to plan ML experiments for hyperparameter sensitivity and component removal?▼

The best way to plan ML experiments for hyperparameter sensitivity is to generate a feasibility-first implementation workflow that evaluates component contributions, estimates compute requirements, and suggests run order cuts under dependency constraints.

How do I know which ablation experiments to skip when preparing model evaluation evidence?▼

During model evaluation, you can identify unnecessary ablations to skip by generating a coverage assessment that maps each planned experiment to the specific reviewer question it answers, prioritizing only studies that isolate critical design choices.

Can I generate a reviewer-aligned ablation plan if my main results show only partial evidence for my claims?▼

Yes, you can generate a reviewer-aligned ablation plan when main results show partial or full evidence, creating targeted component and hyperparameter ablations that answer questions about contribution sensitivity and alternative design choices.

Does ablation planning assess compute feasibility and run order for ML experiments?▼

Yes, ablation planning assesses compute feasibility by reviewing implementation constraints, estimating compute costs for each experiment, and producing a suggested run order with prioritized cuts to ensure experiments are executable.

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