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.
npx skills add claesbackman/AI-research-feedback --skill review-grantgit clone --depth 1 https://github.com/claesbackman/AI-research-feedbackWrote 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.
[](https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-grant)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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)
- US federal science and health:
- If the first token of
$ARGUMENTSmatches 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
$ARGUMENTSas a file path and set the target program/funder tomajor-funder(meaning the review applies high general standards without a specific sponsor persona). - If
$ARGUMENTSis empty, set both to their defaults: no file path (auto-detect) and target program/fundermajor-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:
- 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 areviews/folder. These are outputs of earlier review runs, not proposal materials. - 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, orapplication. - 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.
- 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.
- 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
- Budget and justification: files containing
- 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.
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.
- 12d ago First seen · 472 lines · 0 tokens per session scan A ca9105903eaa
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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