research-refine

Refine vague research directions into implementation-oriented method plans.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the common research bottleneck where a promising idea remains too vague or overbuilt to be actionable, helping you anchor your problem and refine your technical route into a concrete, paper-worthy plan.

Core Features & Use Cases

  • Problem Anchoring: Freezes your core research problem to prevent scope creep and drift during the refinement process.
  • Iterative Review: Uses a specialized reviewer model to stress-test your proposal for technical specificity, frontier leverage, and contribution quality.
  • Use Case: When you have a rough idea for a new model architecture but aren't sure how to validate it, this skill guides you through a multi-round refinement process to produce a focused, implementation-ready experiment roadmap.

Quick Start

Use the research-refine skill to decompose my idea for a new diffusion-based planning agent into a concrete research proposal.

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 research idea into a concrete methodology plan?▼

To turn a vague research idea into a concrete methodology plan, you need iterative review to anchor your core problem and stress-test technical specificity. This prevents scope creep and refines rough concepts into implementation-oriented, paper-ready experiment roadmaps.

What is the best way to design experiments for a new model architecture proposal?▼

Designing experiments for a new model architecture proposal requires problem anchoring to freeze your core research question. A specialized reviewer then evaluates your technical route for frontier leverage and contribution quality to produce a focused, implementation-ready validation roadmap.

How does iterative review improve academic writing and research proposals?▼

Iterative review improves academic writing and research proposals by stress-testing them for technical specificity and contribution quality. This multi-round refinement process ensures your methodology is frontier-aware, technically feasible, and anchored to a specific problem.

Do I need local literature scanning to refine an AI research proposal?▼

Yes, you need local literature scanning to refine an AI research proposal effectively. It ensures your methodology remains frontier-aware and contribution-focused by validating the technical feasibility and novelty of your experiment design against existing work.

Can I use this methodology refinement process for technical research outside of AI?▼

Yes, you can use this methodology refinement process for technical research outside of AI. It applies to any academic or technical research project requiring high-fidelity, frontier-aware methodology and implementation-oriented problem decomposition.

Why does my research direction keep drifting during proposal writing?▼

Your research direction drifts during proposal writing due to a lack of problem anchoring. Freezing your core research problem early prevents scope creep, allowing iterative review to focus on technical specificity rather than constantly redefining the scope.