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Skills/Testing & QA
k-dense-ai avatar

Framing Research Questions

k-dense-ai/science-superpowers

This is the mandatory first step before any data analysis, designed to prevent p-hacking and HARKing by forcing you to write down your research question…

GitHub stars
202
last updated
Jun 1, 2026
View on GitHubSave

Install

$ npx -y skills add k-dense-ai/science-superpowers --skill framing-research-questions --agent claude-code

Installs into .claude/skills of the current project.

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What it does

This is the mandatory first step before any data analysis, designed to prevent p-hacking and HARKing by forcing you to write down your research question, hypotheses, and what counts as an answer before you touch outcome data. It walks through context exploration, clarifying questions, proposes 2-3 framings with tradeoffs, then documents everything in a markdown file you commit to git. The hard gate is real: no loading datasets, no statistics, no plots until your research framing is approved. It's strict about this because once you've seen the data, you can't unsee it, and every subsequent choice becomes contaminated. Even "simple" questions go through this. If you do any kind of confirmatory analysis or care about scientific rigor, this enforces the discipline that keeps results honest.

Files

Select a file.

Details

Repository
github.com/k-dense-ai/science-superpowers
Categories
Testing & QA, Data Science & ML, Debugging
First seen
Jun 11, 2026

More from k-dense-ai/science-superpowers

All 15
  • Investigating Anomalous Results
  • Preregistering Analysis
  • Receiving Critical Review
  • Reporting And Archiving Findings
  • Requesting Red Team Review
  • Setting Up Reproducible Analysis
  • Subagent Driven Analysis
  • Surveying Prior Work
  • Using Science Superpowers
  • Verifying Results Before Claiming
  • Writing Science Skills
  • Designing The Analysis
  • Dispatching Parallel Investigations
  • Executing Analysis

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