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research-idea-clarifier

Restate vague AI research ideas into testable hypotheses and experiment plans.

Updated Apr 23, 2026

Search Tags:#hypothesis-generation #experiment-design #research-direction #idea-clarification #sequence-learning #AI-research

Ready to run

npx skills add https://github.com/Ocean326/Agents --skill research-idea-clarifier

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-idea-clarifier
Source: https://github.com/Ocean326/Agents/tree/main/skills/global/research-idea-clarifier
Command: npx skills add https://github.com/Ocean326/Agents --skill research-idea-clarifier

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Turns vague AI research ideas into testable, goal-directed directions.

Core Features & Use Cases

Restates ideas into task, pain point, proposed change, and expected benefit.
Explicitly frames hypotheses, evaluation criteria, and the first minimal experiment.
Produces a concise contribution summary and 2-3 testable hypotheses, ready for hands-on design.

Quick Start

Feed in your rough AI idea and let the skill output a testable research direction.

Frequently Asked Questions about research-idea-clarifier

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

FAQPage Schema

How do I turn a vague AI research idea into a testable hypothesis?▼

To turn a vague AI research idea into a testable hypothesis, restate the idea by defining the task, pain point, proposed change, and expected benefit. This clarification process explicitly frames the hypothesis and evaluation criteria for subsequent testing.

What is the best way to structure experiment design for AI research?▼

The best way to structure experiment design for AI research is to use a compact decision framework that produces a concise contribution summary, 2-3 testable hypotheses, and defines the first minimal experiment to validate the proposed direction.

How do I frame research directions from sequence learning concepts?▼

Framing research directions from sequence learning concepts involves restating the initial concept into a goal-directed proposal. The mechanism outputs a ready-to-use plan that includes expected benefits and a first minimal experiment for hands-on design.

Can I generate evaluation criteria automatically from a rough AI idea?▼

Yes, you can generate evaluation criteria from a rough AI idea by enforcing a compact decision framework. This process explicitly frames the evaluation criteria alongside testable hypotheses and the expected benefits of the proposed change.

Do I need a fully detailed proposal before clarifying a research direction?▼

No, you do not need a fully detailed proposal before clarifying a research direction. You can feed in a rough, fuzzy AI idea and the task framework will restate it into a structured, goal-directed plan ready for experiment design and audits.