Agent skill

grant-proposal

Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says \"write grant\", \"grant proposal\", \"申請書\", \"write KAKENHI\", \"科研費\", \"基金申请\", \"写基金\", \"NSF proposal\", or wants to turn research ideas into a funding application.

brycew6m4,252★ · +31/wk · 3 repos on radarProfile →
claude-codecan modify filesNOASSERTION
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill grant-proposal --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 28 KB
Bundled scripts: none
Allowed tools: Bash(*)ReadWriteEditGrepGlobWebSearchWebFetchAgentSkillmcp__codex__codexmcp__codex__codex-reply
Path: skills/42-wanshuiyin-ARIS/skills/grant-proposal/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,244
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Drafts a grant proposal from validated research ideas, guiding the user through a multi-phase workflow to produce a reviewer-ready document tailored to a specified grant type and sub-type. It supports multiple funders (KAKENHI, NSF, NSFC, ERC, DFG, SNSF, ARC, NWO, GENERIC) and handles language and formatting requirements. The process includes literature grounding, gap verification, aims design, a claims-evidence matrix, narrative arc planning, timeline milestones, and a structure review before drafting sections.

How it works

  • Phase 0 parses the input arguments to identify the research direction, grant type and sub-type, and any overrides (format, language, review rounds). It also collects context from the project directory (IDEA_REPORT.md, literature notes, publication lists, prior reviews, and a prior GRANT_STATE.json if present).
  • Phase 1 conducts literature and landscape positioning by invoking /research-lit to ground the proposal, then searches for competing funded projects and runs /novelty-check to articulate a gap statement. It then pauses for user confirmation before proceeding unless AUTO_PROCEED is true. The phase ends with writing GRANT_STATE.json (phase: 1) and awaiting user input.
  • Phase 2 designs Narrative Structure & Aims, including: defining 2–4 Specific Aims with deliverables, building a Claims-Aims-Evidence Matrix, crafting a narrative arc (Problem → Why Now → What We Propose → Why It Will Work → What We Will Deliver), and outlining a Timeline & Milestones. It ends with a checkpoint that presents the designed structure and seeks user feedback before drafting.
  • Phase 3 (not fully shown here) would draft the proposal sections per the chosen grant type template, producing complete prose documents rather than outlines.

All outputs are formatted according to the specified OUTPUT_FORMAT (markdown or latex) and LANGUAGE, and use the prescribed directory grant-proposal/ for generated files. The system persists state in grant-proposal/GRANT_STATE.json after each phase and uses phase-based resume logic.

When to use it

Use when a user needs to turn research ideas into a fundable grant application and wants agency-specific structure and language, including KAKENHI, NSF, NSFC, ERC, DFG, SNSF, ARC, NWO, or GENERIC formats. Triggered by phrases like "write grant", "grant proposal", or equivalents in various languages.

What it can touch

  • Commands and tools referenced: /research-lit, /novelty-check, /research-review. These are invoked as part of the workflow to ground the proposal and solicit expert feedback. The skill reads existing files like IDEA_REPORT.md, refine logs, and GRANT_STATE.json from the grant-proposal directory to resume context.

Caveats

  • The workflow requires explicit user confirmation at designated checkpoints unless AUTO_PROCEED is set to true. The process is designed to pause for PI judgment at each stage. State persistence uses grant-proposal/GRANT_STATE.json and may store phase, grant_type, language, and a gap/aims snapshot. No outcomes are promised; the system only outlines structure, alignment, and drafting steps as described.
From the SKILL.md

# Grant Proposal: From Research Ideas to Fundable Application Draft a grant proposal based on: **$ARGUMENTS** ## Overview This skill turns validated research ideas into a structured, reviewer-ready grant proposal. It chains sub-skills into a grant-specific pipeline: ``` /research-lit → /novelty-check → [structure design] → [draft] → /research-review → [revise] → GRANT_PROPOSAL.md (survey) (verify gap) (aims + matrix) (prose) (panel review) (fix) (done!) ``` **This is a parallel branch, not part of the linear Workflow 1→1.5→2→3 pipeline.** After `/idea-discovery` produces validated ideas, the user can either: - Go to `/experiment-bridge` → `/auto-review-loop` → `/paper-writing` (implement & publish) - Go to `/grant-proposal` (write funding application first, then implement after funding) ``` ┌→ /experiment-bridge → /auto-review-loop → /paper-writing (publish track) /idea-discovery ────┤ └→ /grant-proposal → [get funded] → /experiment-bridge → ... (funding track) ``` Grant proposals argue for **future work** (feasibility + potential), not completed work (results + claims). This skill handles the unique requirements of grant writing: narrative arc design, reviewer-facing structure, bu

What's inside
Steps it walks through
  1. Overview
  2. Constants
  3. Grant Type Specifications
  4. KAKENHI (Japan — JSPS)
  5. NSF (US)
  6. NSFC (China — 国家自然科学基金)
  7. ERC (EU — European Research Council)
  8. DFG (Germany — Deutsche Forschungsgemeinschaft)
  9. SNSF (Switzerland — Swiss National Science Foundation)
  10. ARC (Australia — Australian Research Council)
  11. NWO (Netherlands — Dutch Research Council)
  12. GENERIC
  13. State Persistence (Compact Recovery)
  14. Workflow
More from Auto-Empirical-Research-Skills
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About this skill
What does the grant-proposal skill do?

Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says \"write grant\", \"grant proposal\", \"申請書\", \"write KAKENHI\", \"科研費\", \"基金申请\", \"写基金\", \"NSF proposal\", or wants to turn research ideas into a funding application.

How do I install it?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill grant-proposal --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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