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 Aperivue/medsci-skills --skill grant-buildergit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote 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/aperivue/medsci-skills/grant-builder)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/grant-builder"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/grant-builder/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/aperivue/medsci-skills/grant-builder"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/grant-builder.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 | $0.00036 | $0.01593 |
| Opus 5 | $0.00018 | $0.00796 |
| Sonnet 5 | $0.00007 | $0.00319 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
grant-builder 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 10d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grant-Builder Skill
Purpose
This skill supports competitive proposal writing for:
- national R&D grants
- multi-institution consortia
- challenge proposals
- internal pilot funding
- translational medical AI project plans
- Korean government grants (산학협력 / 연구계획서 — MOHW 복지부, MOTIE 산자부, MSS 중기부, and regional industry-academia programs)
It is optimized for projects where clinical relevance, multi-site coordination, and executable milestones matter as much as technical novelty.
Korean Government Grant Mode (산학과제 / 연구계획서)
When the user requests a Korean industry-academia grant (산학과제) or research plan (연구계획서), apply the adaptations below. Korean program terms are preserved in parentheses because they are the literal form used on the funding agency's template.
Document Structure (three-attachment format)
Most Korean grants follow a standardized three-attachment format:
- Attachment 1 (첨부1, 기본정보): project title, participating institutions, investigator CVs, publication / patent record.
- Attachment 2 (첨부2, 매칭확인서): per-institution cost-share confirmation, typically finalized after a kickoff meeting between the institutions.
- Attachment 3 (첨부3, 연구계획서): the 10-page research plan — structure below.
Attachment 3 Standard Structure
1. Significance & Aims (약 2p)
- clinical problem with quantitative framing
- domestic + international trends (3–5 year literature / guideline window)
- differentiation of the proposed work
2. Research Content & Methods (약 4p)
- staged roadmap (Phase 1 – N with time ranges)
- pipeline schematic (mandatory when an AI pipeline is in scope)
- per-subproject institution and personnel assignment
3. Team Capability (약 1p)
- expertise + representative record (SCI papers, patents) per investigator
- cross-institution synergy (hospital = data / clinical; university = algorithm)
4. Expected Outcomes & Utilization (약 2p)
- quantitative targets: SCI papers, patents
- qualitative targets: clinical impact, standardization contribution
- linkage to follow-on larger grants (positioning as a seed)
5. Budget Plan (약 1p)
- RA salaries, computing equipment, consumables, academic activities, indirect costs
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 252 lines · 36 tokens per session scan A 33b7848d79f9
grant-builder is a skill published in the GitHub repository Aperivue/medsci-skills (291 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,593 once invoked, about $0.0002 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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