research-refine

Transform vague research directions into anchored, testable plans with validation artifacts.

Updated Mar 17, 2026
One-click install
npx skills add https://github.com/loujc/Auto-claude-code-research-in-sleep-manual --skill research-refine-loujc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research-refine
Source: https://github.com/loujc/Auto-claude-code-research-in-sleep-manual/tree/main/skills/research-refine
Command: npx skills add https://github.com/loujc/Auto-claude-code-research-in-sleep-manual --skill research-refine-loujc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Turn vague research directions into anchored, implementable plans with a sharp, minimal strategy.

Core Features & Use Cases

  • Anchored Problem: Freeze a clear problem statement to guide all iterations.
  • Phase-based refinement: Progress from initial proposal to focused, testable plan with a minimal set of experiments.
  • Output artifacts: Final anchored proposal, a refinement log, and an experiment-ready roadmap.

Quick Start

Provide a vague research direction and I will return an anchored problem, a minimal, concrete method proposal, and a validation plan.

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 direction into an implementable research plan?

To turn a vague research direction into an implementable research plan, you provide your initial idea to establish an anchored problem statement. This frozen anchor then guides stepwise refinement to produce a focused method proposal and a minimal validation plan.

What is an anchored problem in research proposal planning?

An anchored problem in research proposal planning is a clearly frozen problem statement that guides all subsequent iterations. It ensures your research direction remains focused while you decompose ideas into testable steps and minimal validation artifacts.

How do I create a minimal validation plan for machine learning experiments?

You create a minimal validation plan by freezing your problem anchor and applying phase-based refinement across rounds. This structured workflow progressively narrows your initial proposal into an experiment-ready roadmap with a minimal set of testable steps.

Can I use structured refinement for applied systems research?

Yes, you can use structured refinement for applied systems research across diverse disciplines. The methodology enforces a fixed anchor and structured output format to decompose ideas into focused method plans and minimal validation artifacts.

What is the best way to refine a research proposal without losing the original focus?

The best way to refine a research proposal without losing focus is to enforce a fixed anchor across rounds. This approach freezes a clear problem statement to guide all iterations, ensuring phase-based refinement yields a testable plan rather than drifting.

What outputs should I expect from an AI-assisted research refinement workflow?

Outputs from an AI-assisted research refinement workflow include a final anchored proposal, a detailed refinement log, and an experiment-ready roadmap. These artifacts provide a concise problem frame and testable steps for immediate implementation.