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Grant Proposal Assistant

This walks you through writing NIH, NSF, and foundation grant proposals by forcing you to answer the four questions reviewers actually care about: testable hypothesis, why now, what's genuinely new, and can you actually do it. The workflow is structured around an eight-step checklist covering everything from specific aims to compliance, with section-by-section frameworks that show exactly what goes in significance versus innovation versus approach. It includes a specific aims template, reviewer mindset guidance, and an evaluation rubric requiring a minimum 3.5 average score. Use it when drafting R01s, revising after getting triaged, or reality-checking whether your "innovative approach" statement actually says anything specific.

skills.sh installs
353
GitHub stars
193
last updated
Aug 21, 2026

Install

$ npx -y skills add lyndonkl/claude --skill grant-proposal-assistant --agent claude-code

Installs into .claude/skills of the current project.

Files

Grant Proposal Assistant

Table of Contents

Related skills (not this one):

  • Manuscripts: scientific-manuscript-review
  • Fellowship personal statements: career-document-architect
  • Letters of recommendation: academic-letter-architect

Core Questions

Every grant proposal must convincingly answer these four questions:

1. What is the central hypothesis?

  • Testable, specific, falsifiable
  • Not just "we will study X" but "we hypothesize that X causes Y through mechanism Z"

2. Why is the problem important NOW?

  • What gap exists in current knowledge?
  • Why is this gap significant for the field/patients/society?
  • Why is this the right time (new tools, preliminary data, shifting paradigm)?

3. What makes the approach innovative?

  • What is genuinely new (concept, method, application)?
  • How does this advance beyond incremental improvement?
  • Innovation in approach AND/OR innovation in what will be learned

Sponsors

Details

Repository
github.com/lyndonkl/claude
Categories
Git & Pull Requests, Documentation, Code Review & Quality, Data Science & ML
First seen
Jun 3, 2026

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