k2f-grant-proposal
Turn research ideas, preliminary data, and agency solicitations into reviewer-ready grant proposals with a fundable hypothesis, independent specific aims, and agency-aligned significance and innovation. Use when the user asks for a grant proposal, specific aims page, NIH R01/R21/K, NSF proposal, or foundation grant narrative. Create grant proposals with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI agents, K2F lets them focus on semantic content and design intent while automatically handling layout, typography, and formatting complexity.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/suzheng/k2f --skill k2f-grant-proposal- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads k2f-grant-proposal automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
K2F Grant Proposal — AI-Native Aesthetic Design
Purpose
Teach agents to blueprint fundable grant narratives before layout: a falsifiable hypothesis, explicit gaps, independent aims, reviewer-scannable hierarchy, and honest feasibility—without inventing data or drowning reviewers in prose.
Built on K2F
This skill produces grant packages as .K2F documents. Follow the k2f skill for authoring: unpack or init a workspace, edit the semantic tree, run pack/verify, then export. K2F keeps agents on meaning (section roles, aims, evidence)—not pixel coordinates—while the engine compiles layout, typography, and theme into consistent output across PDF, DOCX, and other formats.
Before you build, install the core skill: npx skills add suzheng/k2f --skill k2f.
When to Use
- Drafting or restructuring NIH, NSF, ERC, or foundation proposals from notes or prior work.
- Rebuilding a weak Specific Aims page before the full research strategy.
- Aligning content depth and visual hierarchy to mechanism limits and review rubrics.
Workflow
Step 1. Generate the content_and_design.md
Single blueprint for semantic content and aesthetic intent. Structure it with labeled blocks the k2f skill can map to pages later.
Sub-steps
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Mechanism lock — Agency, solicitation ID, mechanism, page limits, required sections, scoring criteria (NIH: Significance, Innovation, Approach, Investigators, Environment; NSF: Intellectual Merit, Broader Impacts). Drop optional material that exceeds limits.
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Four-question audit — Answer in prose using user-supplied evidence only:
- Central hypothesis: testable, falsifiable (not “we will study X”).
- Why now: gap, urgency, new tools or data.
- Innovation: conceptual, methodological, application, or novel outcomes—each with specifics.
- Feasibility: team, timeline, resources, aim independence, pitfalls with Plan B.
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Specific Aims architecture — One page, ~400–550 words. Narrative arc: hook → gap → long-term goal → hypothesis → 2–3 verb-led aims (testable, largely independent) → impact close. Per aim: 2–3 sentences, expected outcome, no fatal dependency on prior aim success.
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Strategy outline — Significance (problem, state of field, gap, cost of not knowing, if-successful impact). Innovation as scannable bullets (“This project is innovative because…”). Approach per aim: rationale → strategy/controls/stats → expected outcomes → pitfalls/alternatives → milestones. Assign word budgets from page limits (~450 words per full strategy page); note where figures replace paragraphs (preliminary data, workflow, timeline).
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Design intent — Document tone, hierarchy, density, palette for the engine:
- Tone: formal, evidence-led; a non-expert primary reviewer grasps significance on page 1.
- Hierarchy: Aims page = strongest contrast and largest headings; strategy H1s conclusion-driven (“Gap in X limits Y”) not generic labels.
- Density: bullet-led Innovation; Approach subheads per aim; no paragraphs over ~6 lines.
- Palette: calm academic (navy/slate body, one accent for aim numbers and key metrics); WCAG AA; no decorative clutter on the Aims page.
Rules & constraints
- Golden thread: problem → hypothesis → aims → approach → impact; cut interesting tangents.
- Aims independence: partial success must remain fundable; merge aims that share one point of failure.
- Honesty gate: mark missing preliminary data; never fabricate citations, pilot results, or budget figures.
- Agency fit: mirror funder vocabulary and review dimensions in outline order.
Common failure modes
- Fishing expedition without falsifiable hypothesis → rewrite as if/then with measurable endpoints.
- Methods-before-problem Aims page → reorder hook → gap → hypothesis first.
- “Innovative approach” without novelty → one bullet per concrete first/novel/application.
- Scope creep → fewer aims or narrower mechanism; align word budgets.
- Aim 2 impossible if Aim 1 fails → redesign independence or alternatives.
- Dense Aims essay → enforce aim cards, bullets, and word cap in the design doc.
Quality checklist
- Hypothesis on Aims page matches Approach.
- Each aim has pitfalls + alternatives; no single point of failure across aims.
- Significance states gap and cost of not knowing.
- Innovation bullets name concrete novelty, not superlatives.
- Word budgets sum under mechanism limits.
- All evidence traceable to user inputs.
Step 2. Use the content_and_design.md and k2f skill to generate a K2F file and export to an appropriate file format.
Follow k2f skill to instantiate pages from the blueprint, apply roles from design intent, verify, and export the agency-ready deliverable.
FAQ
What does the k2f-grant-proposal skill do?
Turn research ideas, preliminary data, and agency solicitations into reviewer-ready grant proposals with a fundable hypothesis, independent specific aims, and agency-aligned significance and innovation. Use when the user asks for a grant proposal, specific aims page, NIH R01/R21/K, NSF proposal, or foundation grant narrative. Create grant proposals with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI agents, K2F lets them focus on semantic content and design intent while automatically handling layout, typography, and formatting complexity.
How do I install k2f-grant-proposal?
Run npx skills add https://github.com/suzheng/k2f --skill k2f-grant-proposal in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/k2f-grant-proposal/ (personal) or .claude/skills/k2f-grant-proposal/ (project).
Which AI agents can use k2f-grant-proposal?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is k2f-grant-proposal safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains k2f-grant-proposal?
It is published by suzheng in suzheng/k2f. It has 2,557 recorded installs.
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