research-refine

Refine vague ML/AI research directions into implementable method plans.

2|1|Updated Apr 19, 2026

Search Tags:#paper writing #research refinement #literature grounding #checkpoint recovery #claim-driven validation #method proposal #ML training plan

Ready to run

npx skills add https://github.com/raja21068/AutoResearch --skill research-refine-raja21068

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-refine
Source: https://github.com/raja21068/AutoResearch/tree/main/skills/aris/research-refine
Command: npx skills add https://github.com/raja21068/AutoResearch --skill research-refine-raja21068

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you convert a vague research direction into a concrete, implementable, frontier-aware research method anchored to a single clear problem and validation plan.

Core Features & Use Cases

Problem Anchor Preservation: Freezes an immutable problem statement and reuses it across iterative revisions to prevent drift.
Method-First Refinement Loop: Scans grounding literature (local and optional web), identifies a technical gap, proposes the smallest adequate mechanism, and then iteratively reviews and tightens the plan.
Elegance + Frontier-Aware Review: Uses an iterative reviewer loop to enforce parsimony, contribution focus, and appropriate use of foundation-model-era primitives.
Claim-Driven Validation Sketch: Produces minimal experiments that directly test core claims with focused baselines/ablations.

Quick Start

Ask to refine an approach with the command: /research-refine "problem | approach".

Frequently Asked Questions about research-refine

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

FAQPage Schema

How do I turn a vague ML research idea into a concrete method proposal?▼

Claim-driven validation produces minimal experiments that directly test core claims with focused baselines and ablations. This approach ensures your validation sketch tests the fundamental mechanism without creating an overbuilt benchmark plan.

How do I prevent scope drift when iteratively refining a research plan?▼

Frontier-aware review is appropriate when your method plan needs to enforce parsimony, maintain contribution focus, and appropriately use foundation-model-era primitives. It iteratively reviews and tightens your proposal to ensure elegance and minimal adequate mechanisms.

What is the best way to sketch minimal experiments for an AI paper?▼

The best way to sketch minimal experiments for an AI paper is to use claim-driven validation. This produces focused baselines and ablations that directly test core claims, avoiding overbuilt benchmark plans while satisfying implementation-oriented proposal requirements.

Can I recover and resume my research proposal generation from a checkpoint?▼

Yes, you can recover and resume your research proposal generation from a checkpoint. The refinement process produces checkpoint-resumable output artifacts, allowing you to pause and resume the iterative reviewer-based refinement without losing your anchored problem statement.