framing-research-questions

Name: framing-research-questions
Availability: InStock
Author: K-Dense

Converts vague research ideas into precise, falsifiable framing documents with hypotheses and success criteria.

282|26|Updated May 28, 2026

Search Tags:#workflow #hypotheses #pre-registration #framing #research-questions

Ready to run

npx skills add https://github.com/K-Dense-AI/science-superpowers --skill framing-research-questions

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: framing-research-questions
Source: https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/framing-research-questions
Command: npx skills add https://github.com/K-Dense-AI/science-superpowers --skill framing-research-questions

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Turning fuzzy research interests into precise, falsifiable questions with explicit hypotheses, data requirements, and decision rules to guide investigations from the outset.

Core Features & Use Cases

Guides researchers from context understanding to framing, hypothesis specification, and pre-registration.
Provides a structured process to generate 2-3 framings, compare them, and produce a formal question document for review.
Automatically saves the framed question as a documented artifact in docs/science-superpowers/questions.

Quick Start

Draft an initial framing plan by outlining context, proposing 2–3 framings, and writing the question document to docs/science-superpowers/questions/YYYY-MM-DD-framing-research-questions.md, then submit for partner review.

Frequently Asked Questions about framing-research-questions

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

FAQPage Schema

How do I turn vague research interests into falsifiable hypotheses?▼

To turn vague research interests into falsifiable hypotheses, you need a structured framing process that defines context, generates 2-3 framings, and specifies explicit hypotheses with data requirements and decision rules before analysis begins.

What is pre-registration in research and when do I need it?▼

Pre-registration in research is committing to a formal question document with hypotheses, data needs, and success criteria before data analysis. You need it to ensure falsifiable framing and prevent hypothesis drift in experiments, literature surveys, or data-integration tasks.

How do I write a formal research question document for partner review?▼

Write a formal research question document by outlining context, comparing 2-3 framings, specifying hypotheses, data needs, and success criteria, then saving it as a markdown file under your questions directory and submitting it for a partner-review gate.

Can I use structured framing for literature surveys and data-integration tasks?▼

Structured framing applies to literature surveys and data-integration tasks across sciences. It guides you from context understanding to framing and hypothesis specification, ensuring your investigation has explicit data requirements and decision rules regardless of the research type.

What's the best way to compare different research framings before analysis?▼

The best way to compare research framings is generating 2-3 distinct options, evaluating their hypotheses, data needs, and success criteria side by side, then selecting the strongest framing to commit to a pre-registration document before proceeding to data analysis.

Why do I need a partner-review gate before data analysis?▼

A partner-review gate ensures your framed research questions, hypotheses, and success criteria are precise and falsifiable before analysis begins. It prevents vague framings from proceeding and commits you to a documented question document for research integrity.