Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Wesley-Yin77/nsfc_medicine_allnpx agentmods add skills/wesley-yin77/nsfc_medicine_all/nsfc-literatureWrote 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/wesley-yin77/nsfc_medicine_all/nsfc-literature)<a href="https://agentmods.dev/skills/wesley-yin77/nsfc_medicine_all/nsfc-literature"><img src="https://agentmods.dev/badge/skills/wesley-yin77/nsfc_medicine_all/nsfc-literature/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/wesley-yin77/nsfc_medicine_all/nsfc-literature"><img src="https://agentmods.dev/badge/skills/wesley-yin77/nsfc_medicine_all/nsfc-literature.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 | $0.00087 | $0.02421 |
| Opus 5 | $0.00044 | $0.01210 |
| Sonnet 5 | $0.00017 | $0.00484 |
| Haiku 4.5 | $0.00009 | $0.00242 |
Grade A, and why
nsfc-literature 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NSFC Literature — 立项依据撰写器
辅助撰写NSFC标书中最关键的"立项依据"部分,基于已中标标书的漏斗式递进范式。
Routing protocol
1. Load the manifest and core layer
Read manifest.yaml. Always load:
../../../_shared/core/winning-patterns.md— 漏斗结构与中标模式../../../_shared/core/terminology.md— 术语一致性账本../../../_shared/core/ethics.md— AI辅助边界static/core/stance.md— 默认立场
2. Detect axes
| Axis | Values | Method |
|---|---|---|
grant_type |
面上 / 青年 / 地区 | 从用户描述检测 |
stage |
大纲 / 草稿 / 终稿 | 从现有内容检测 |
discipline |
基础医学 / 临床医学 / 预防医学 / 药学 / 中医药 | 从研究领域检测 |
3. Load matching fragments
grant_type→static/fragments/grant_type/{面上,青年,地区}.mddiscipline→static/fragments/discipline/{basic,clinical,preventive,pharmacy,tcm}.md
4. Apply the funnel framework
逐层构建立项依据的5层漏斗结构。【强制】必须分节!
⚠️ 核心铁律:22/22份中标标书的立项依据均采用分节递进结构。立项依据不能是一整块连续文字,必须用带编号的独立标题将内容拆分为4-6个小节。每节有一个明确的论证任务。
⚠️ 【强制检查项】前期工作基础分散渗透原则
前期工作基础/预实验数据必须分散在2-4节中作为论证证据,而非集中在一节用(1)(2)(3)(4)(5)Results式罗列。违反此原则会导致评审专家阅读疲劳,且显得像论文Results而非标书论证。
正确做法:每项前期发现以"问题引领"方式嵌入论证(如"CIP2A过表达的临床意义如何?为回答这一问题,申请人以..."),而非独立编号罗列。
错误做法:在某一节集中列出"(1)单细胞层面...(2)多队列层面...(3)表观组学层面..."的Results式清单。
⚠️ 【强制检查项】立项依据中的图类型规范
立项依据中只能放机制/通路示意图,不能放预实验数据组合图。预实验数据图(WB条带/统计图/散点图等)放在研究基础部分。
图编号 位置 允许的图类型 禁止的图类型 图1 Layer 2或3 信号通路图(已知通路+探索部分虚线标注) ❌ 预实验数据图 图2 Layer 3或4 机制示意图(核心分子功能,虚实线区分已知/待验证) ❌ 预实验数据图 图3 Layer 5末尾 机制假说图(全文假说总结,实线=已验证+虚线=拟验证) ❌ 预实验数据图 参见
../nsfc-figure/static/core/figure-taxonomy.md的图类型分类。
分节模板(强制使用)
立项依据
├─ 1. [疾病]的临床挑战与[领域]的研究现状
│ ├─ 流行病学数据(发病率/死亡率/现有治疗局限)
│ └─ 为什么要从[XX角度]研究该疾病
│
├─ 2. [核心领域/通路]在[疾病]中的研究进展
│ ├─ 该通路/领域的已知认知
│ ├─ 关键分子的已有功能证据
│ └─ ⚠ 此处开始引入1-2项预实验数据作为论据
│
├─ 3. [核心分子]在[疾病]中的功能与机制证据
│ ├─ 该分子的已知功能(基因→蛋白→通路→表型)
│ ├─ ⚠ 本课题组的前期发现(预实验数据2-3项)
│ └─ ⚠ 在此节中必须放置至少1张信号通路/机制示意图
│
├─ 4. [核心分子]调控[疾病]的潜在机制
│ ├─ ⚠ 更深入的机制层面分析和前期生信/实验数据
│ ├─ 已有证据的不足以回答关键问题
│ └─ ⚠ 在此节中放置第二张机制示意图或预实验数据图
│
└─ 5. 小结与科学假说
├─ 总结前文完整逻辑链
├─ "由此,我们提出如下科学假说:……"(必须用引号框出)
└─ 简要验证策略 + 研究意义
What ships with it
2 files 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.
- 12d ago First seen · 161 lines · 87 tokens per session scan A 94948f8fd002
nsfc-literature is a skill published in the GitHub repository Wesley-Yin77/nsfc_medicine_all (63 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 2,421 once invoked, about $0.0004 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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