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ablation-planner

Generate an Ablation Plan with component changes and hyperparameter sweeps.

1|Updated Mar 24, 2026

Search Tags:#ablation #machine-learning #experimental-design #research-planning #manuscript-submission #reviewer-perspective #experimental-logs

Ready to run

npx skills add https://github.com/kitcaf/skills --skill ablation-planner-kitcaf

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ablation-planner
Source: https://github.com/kitcaf/skills/tree/main/skills/skills-codex/skills/ablation-planner
Command: npx skills add https://github.com/kitcaf/skills --skill ablation-planner-kitcaf

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ablation planning is a time-consuming, error-prone task in academic workflows; this skill designs ablations from a reviewer perspective and coordinates between planner and executor to produce a robust evidence plan.

Core Features & Use Cases

Systematically design ablations that address reviewer questions and strengthen manuscript submissions.
Delegate the design to a secondary Codex reviewer agent while the local executor assesses feasibility and implements.
Generate a structured Ablation Plan including component removals/replacements, hyperparameter sweeps, and estimated compute, ready for recording in EXPERIMENT_LOG.md.

Quick Start

Provide project context and the current result-to-claim status to generate a prioritized ablation plan.

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 that address reviewer questions for machine learning manuscripts?▼

Designing ablations from a reviewer perspective involves coordinating a secondary Codex reviewer agent with a local executor to assess feasibility and implement changes. It produces a structured Ablation Plan with component modifications, hyperparameter explorations, and estimated compute for manuscript submission.

What is the best way to plan machine learning experimental ablations for manuscript submission?▼

The best way to plan experimental ablations is applying this skill when main results pass claim-supported criteria. It guides reviewer-focused designs and feasibility checks between a reviewer agent and local executor, outputting an Ablation Plan suitable for recording in EXPERIMENT_LOG.md.

Do I need main results to pass claim-supported criteria before planning ablations?▼

Yes, you need main results to pass claim-supported criteria before planning ablations. The skill applies specifically when this status is met, guiding reviewer-focused experimental designs and feasibility checks to validate the claims your manuscript makes.

Can I use a reviewer agent to check ablation feasibility while a local executor implements it?▼

Yes, delegating ablation design to a secondary Codex reviewer agent while the local executor assesses feasibility and implements changes is supported. This coordination produces a structured Ablation Plan with component-level changes, hyperparameter explorations, and estimated compute.

What components should an ablation plan include for machine learning research?▼

An ablation plan should include component-level changes like removals or replacements, hyperparameter explorations, and estimated compute. It generates a structured record suitable for EXPERIMENT_LOG.md to validate claims and strengthen your manuscript submission against reviewer questions.