review-grant

review-grant is a skill for Claude Code from claesbackman/AI-research-feedback. It costs 25 tokens per session (4,871 once invoked), scanned A, original, MIT.

A six-part review process for checking a grant proposal before submission to a funder or research programme such as NSF, NIH, ERC or Horizon Europe.

In plain words
What is it for?
Use it to review a proposal file, collect feedback from six specialised reviewers, and produce one structured report.
Why use it?
It helps find weaknesses and missing details before reviewers at the funding organisation see the proposal. The review can use either a named funder’s standards or broad proposal standards.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to review a proposal file, collect feedback from six specialised reviewers, and produce one structured report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/claesbackman/ai-research-feedback/review-grant
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add claesbackman/AI-research-feedback --skill review-grant
Clone the repo
git clone --depth 1 https://github.com/claesbackman/AI-research-feedback

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for review-grant

README.md
[![agentmods](https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-grant/github.svg)](https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-grant)
Your own site
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-grant"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-grant/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for review-grant

Your own site · 80×15
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-grant"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-grant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,871 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.04871
Opus 5 $0.00013 $0.02436
Sonnet 5 $0.00005 $0.00974
Haiku 4.5 $0.00003 $0.00487

Measured 12d ago against content hash ca9105903eaa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

review-grant scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Skills/review-grant/SKILL.md · 472 lines

How it starts

The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are coordinating a rigorous pre-submission review of a grant proposal. You will run 6 specialized review agents in parallel and consolidate their findings into a structured report.

Phase 1: Parse Arguments and Discover the Proposal

Parse $ARGUMENTS as follows:

  • The recognized target programs/funders are:
    • US federal science and health: NSF, NIH
    • International research funders: ERC, HorizonEurope
    • General proposal standards: major-funder, foundation
    • (case-insensitive; users can add further programs or funders by editing this list in the skill file)
  • If the first token of $ARGUMENTS matches one of these names, treat it as the target program/funder and treat any remaining text as the main proposal file path.
  • If no token matches one of these names, treat the entire $ARGUMENTS as a file path and set the target program/funder to major-funder (meaning the review applies high general standards without a specific sponsor persona).
  • If $ARGUMENTS is empty, set both to their defaults: no file path (auto-detect) and target program/funder major-funder.

Store the resolved target program/funder as TARGET_PROGRAM for use in Agent 6 and the report header.

If a file path was provided, use it as the main proposal file. Otherwise, auto-detect:

  1. Search the current directory recursively for likely proposal files with common extensions: *.md, *.txt, *.tex, *.docx, *.pdf (exclude hidden folders, .git, build output, and dependency directories). Also exclude previous review reports and AI-generated commentary: GRANT_PROPOSAL_REVIEW_*.md, PRE_SUBMISSION_REVIEW_*.md, QUICK_REVIEW_*.md, PAP_REVIEW_*.md, code_review_report*.md, and anything inside a reviews/ folder. These are outputs of earlier review runs, not proposal materials.
  2. Prioritize files whose names suggest they are the main narrative, such as those containing proposal, project-description, research-plan, specific-aims, narrative, case-for-support, or application.
  3. Identify the main proposal document: the file that appears to contain the core project narrative rather than only a budget, CV, biosketch, appendix, or letter. If more than one file looks plausible, prefer the one with the clearest summary/abstract and the most complete proposal sections.
  4. Read the main proposal file and identify references to supporting documents, appendices, attachments, supplementary materials, budget files, timeline files, biosketches/CVs, facilities/resources statements, data-management plans, mentoring plans, or letters of support.
  5. Search recursively for common supporting files and record them if present:
    • Budget and justification: files containing budget, justification
    • Timeline and workplan: files containing timeline, gant, gantt, milestone, workplan
    • Personnel documents: files containing biosketch, cv, resume, personnel, team
    • Compliance/supporting plans: files containing data-management, data sharing, management plan, mentoring, facilities, resources, support letter, letter
    • Appendices and supplements: files containing appendix, supplement, supplementary
  6. Record:
    • Full path of the main proposal file
    • Full path of each supporting file and its likely role
    • Proposal title, PI(s)/team, abstract/summary if available
    • Any explicit funding call, solicitation, or sponsor named in the materials

If the proposal is in a binary format such as .pdf or .docx and the environment cannot read it directly, review what is accessible and explicitly note the limitation in the final report.

Read the full file on GitHub · 472 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 472 lines · 0 tokens per session scan A ca9105903eaa

Subscribe to this mod's changes

review-grant is a skill published in the GitHub repository claesbackman/AI-research-feedback (478 stars, last pushed 15d ago), licensed MIT. It adds 25 tokens to every session and 4,871 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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