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Grant Proposal

Community
Immortalqx
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.

Overview

PublisherImmortalqx
Repositorymy_codex_skills
Skill namegrant-proposal
Stars
56
Forks
0
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Immortalqx on GitHub. Read the source before you install it.

Installation

Install the Grant Proposal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Immortalqx/my_codex_skills/tree/main/grant-proposal
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Immortalqx/my_codex_skills.git /tmp/my_codex_skills
mkdir -p .claude/skills
cp -r /tmp/my_codex_skills/grant-proposal .claude/skills/grant-proposal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grant Proposal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Grant Proposal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Grant Proposal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Grant Proposal

Draft a grant proposal based on: $ARGUMENTS

Standalone Scope

This skill works from the user's request, local project files, and any publicly accessible literature or funder pages that can be reached in the current environment.

No local API key is required by this skill. If network access is unavailable, continue from user-provided materials and clearly mark literature or funded-project coverage gaps.

Defaults

  • GRANT_TYPE = KAKENHI unless the request names another agency.
  • GRANT_SUBTYPE = auto from the request or the most common subtype.
  • OUTPUT_FORMAT = markdown unless latex is requested.
  • OUTPUT_DIR = grant-proposal/ for generated files.
  • LANGUAGE = auto:
    • KAKENHI -> Japanese
    • NSFC -> Chinese
    • NSF/ERC/DFG/SNSF/ARC/NWO -> English unless requested otherwise
    • GENERIC -> match the user's language
  • AUTO_PROCEED = false. Stop at major planning checkpoints unless the user explicitly asks for a complete one-shot draft.

Supported grant types:

TypeCommon SectionsNotes
KAKENHI研究目的, 研究計画・方法, 準備状況, 人権の保護 if applicableFormal Japanese academic style; emphasize academic significance, originality, feasibility, and societal value.
NSFProject Summary, Project Description, References, Biosketch, Budget Justification, Data Management PlanExplicit Intellectual Merit and Broader Impacts.
NSFC立项依据, 研究内容, 研究目标, 研究方案, 可行性分析, 创新点, 预期成果, 研究基础Formal Chinese; position against the international frontier and applicant foundation.
ERCExtended Synopsis, Scientific Proposal Part B2High-risk/high-gain framing, work packages, deliverables, milestones.
DFGState of the Art, Objectives, Work Programme, Bibliography, CVEnglish or German depending on request.
SNSFSummary, Research Plan, Timetable, BudgetScientific relevance, originality, feasibility, track record.
ARCProject Description, Feasibility, Benefit, BudgetResearch quality, feasibility, and benefit.
NWOSummary, Proposed Research, Knowledge UtilisationScientific quality, innovation, knowledge utilisation.
GENERICUser-provided or inferred sectionsAsk for page limits and required sections if unknown.

Inputs to Gather

Read only what is useful for the requested proposal:

  1. The research idea, draft, or direction from $ARGUMENTS.
  2. Local files that look relevant, for example:
    • IDEA_REPORT.md, FINAL_PROPOSAL.md, EXPERIMENT_PLAN.md
    • NARRATIVE_REPORT.md, STORY.md, literature.md, survey.md
    • publications.md, cv.md, bio.md, CV.pdf
  3. User-provided grant constraints:
    • agency, subtype, page limit, deadline, language, section template
    • PI stage, team, institution, budget range, project duration
  4. Public sources if network access is available:
    • funder instructions and official criteria
    • recent papers and surveys in the area
    • funded-project databases for related awards

If critical PI-specific or budget information is missing, use explicit placeholders such as [TODO: add PI publications] and [AMOUNT]. Never invent these details.

Workflow

Phase 1: Parse and Scope

Extract:

  • grant agency and subtype
  • output language and format
  • project duration
  • topic, method, application area, and expected contribution
  • any hard requirements such as page limits or mandatory sections

If the request is too vague to draft responsibly, ask concise questions for the missing grant type, project duration, PI background, or target language.

Phase 2: Landscape and Gap

Build the proposal's positioning directly:

  1. Summarize the research area in 5-8 bullets.
  2. Identify the scientific or technical gap.
  3. Search or inspect supplied literature for close work.
  4. Search funded-project pages when relevant and available:
    • KAKENHI: KAKEN database
    • NSF: NSF Award Search
    • NSFC: public NSFC project information where accessible
    • other agencies: official award/project search or public web pages
  5. Write a one-sentence gap statement:
text
Despite progress in [X], [specific gap] remains unresolved because [reason].
This project addresses the gap by [approach], enabling [expected scientific or societal impact].

Checkpoint unless one-shot drafting was requested:

markdown
## Positioning Checkpoint

- Grant type: [type/subtype]
- Proposed title: [working title]
- Gap statement: [gap]
- Closest related work/projects: [3-5 bullets]
- Proposed angle: [1-2 bullets]

Please confirm or adjust before I draft the proposal structure.

Phase 3: Aims and Project Architecture

Design 2-4 aims. Each aim should be:

  • independently valuable
  • technically concrete
  • feasible within the project duration
  • linked to a deliverable
  • paired with a risk and mitigation

Create an aims matrix:

markdown
| Aim | Key Question | Approach | Evidence/Preparation | Risk | Mitigation | Deliverable |
|---|---|---|---|---|---|---|
| Aim 1 | ... | ... | ... | Low/Med/High | ... | ... |

Then create a timeline:

markdown
| Period | Aim/Task | Milestone | Deliverable |
|---|---|---|---|
| Year 1 Q1-Q2 | ... | ... | ... |

Checkpoint unless one-shot drafting was requested:

markdown
## Structure Checkpoint

- Aim 1: [title]
- Aim 2: [title]
- Aim 3: [title if any]
- Main risk: [risk]
- Timeline: [summary]

Proceed to full drafting, or revise the aims first?

Phase 4: Draft the Proposal

Write complete prose in the agency-specific style. Do not leave outline-only sections unless the user explicitly requested an outline.

Recommended drafting order:

  1. Specific Aims / Research Objective
  2. Background / Significance / State of the Art
  3. Research Plan / Methods
  4. Expected Outcomes and Deliverables
  5. Timeline and Milestones
  6. Risk Management and Alternative Plans
  7. PI Qualification / Preparation Status
  8. Budget Justification
  9. Broader Impacts / Societal Significance / Knowledge Utilisation when required

For figures, create text placeholders unless the user asks you to generate them:

markdown
[Figure 1: Overall project architecture showing Aim 1 -> Aim 2 -> Aim 3 and deliverables.]
[Figure 2: Timeline and milestones.]

If the user asks for figures, generate simple local SVG, Mermaid, draw.io, or matplotlib figures directly in the project. Do not call another skill.

Phase 5: Self-Check and Revise

Review the draft directly against the grant type's criteria:

  • Does the opening state the gap early and clearly?
  • Are aims concrete, feasible, and not mutually dependent in a fragile way?
  • Are novelty and significance supported by cited literature or supplied evidence?
  • Are risks and mitigation plans specific?
  • Are deliverables measurable?
  • Does the language match the agency style?
  • Are budget amounts and PI credentials left as placeholders when unknown?
  • Are all citations real or marked [VERIFY]?
  • Are page-limit or required-section constraints respected?

Apply obvious fixes before presenting the final output. If a weakness cannot be fixed without user information, mark it as a clear TODO.

Output Files

Default Markdown output:

text
grant-proposal/
├── GRANT_PROPOSAL.md
├── GRANT_NOTES.md
├── GRANT_STATE.json
├── references.bib
└── figures/

LaTeX output, if requested:

text
grant-proposal/
├── main.tex
├── sections/
│   ├── aims.tex
│   ├── background.tex
│   ├── research_plan.tex
│   ├── timeline.tex
│   ├── pi_qualification.tex
│   └── budget.tex
├── references.bib
└── figures/

If overwriting an existing final file, preserve the previous version by writing a timestamped copy first, for example GRANT_PROPOSAL_YYYYMMDD_HHmmss.md.

GRANT_NOTES.md should contain:

  • assumptions
  • missing user information
  • literature/funded-project coverage limitations
  • TODO placeholders to fill before submission

GRANT_STATE.json should contain only simple recovery metadata:

json
{
  "grant_type": "NSFC",
  "grant_subtype": "Youth",
  "language": "Chinese",
  "status": "drafted",
  "last_updated": "YYYY-MM-DDTHH:MM:SS"
}

Key Rules

  • Do not fabricate budget amounts.
  • Do not fabricate PI information, publications, awards, affiliations, or credentials.
  • Do not hallucinate citations. Use supplied sources or publicly verified sources; otherwise mark [VERIFY].
  • Do not claim novelty from absence of evidence. If search coverage is weak, say so.
  • Grant proposals argue for future work. Emphasize what will be done, why it matters, and why the team can execute it.
  • Keep agency norms visible: KAKENHI expects 社会的意義, NSF expects Broader Impacts, NSFC expects 国际前沿 positioning and 研究基础.
  • Stop for user confirmation at major checkpoints unless the user explicitly requested a one-shot full draft.

Parameter Examples

text
/grant-proposal "robotics foundation model safety — NSF CAREER, English, 5 years"
/grant-proposal "低空通信感知一体化 — NSFC 青年, 中文, 3 years"
/grant-proposal "multimodal robot planning — KAKENHI Start-up, Japanese"
ParameterDefaultDescription
grant typeKAKENHIAgency: KAKENHI, NSF, NSFC, ERC, DFG, SNSF, ARC, NWO, GENERIC
grant subtypeautoSubtype such as Start-up, Wakate, CAREER, Youth
output formatmarkdownmarkdown or latex
languageautoOutput language override
durationinferredProject length and timeline granularity
auto proceedfalseSkip checkpoints only when explicitly requested

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Grant Proposal AI 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.

Why use Grant Proposal on TypingMind?

Because you install it once and use it with any model. Grant Proposal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Grant Proposal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Immortalqx/my_codex_skills/tree/main/grant-proposal. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Grant Proposal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Grant Proposal?

As many as you like. As long as a model supports skills, you can use Grant Proposal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Grant Proposal AI skill free?

Yes. It is published on GitHub by Immortalqx under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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