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 HuiyuLi-2000/Chinese-Grant-Writer-Skills --skill fund-research-content-writergit clone --depth 1 https://github.com/HuiyuLi-2000/Chinese-Grant-Writer-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/huiyuli-2000/chinese-grant-writer-skills/fund-research-content-writer)<a href="https://agentmods.dev/skills/huiyuli-2000/chinese-grant-writer-skills/fund-research-content-writer"><img src="https://agentmods.dev/badge/skills/huiyuli-2000/chinese-grant-writer-skills/fund-research-content-writer/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/huiyuli-2000/chinese-grant-writer-skills/fund-research-content-writer"><img src="https://agentmods.dev/badge/skills/huiyuli-2000/chinese-grant-writer-skills/fund-research-content-writer.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 | $0.00064 | $0.01491 |
| Opus 5 | $0.00032 | $0.00745 |
| Sonnet 5 | $0.00013 | $0.00298 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
fund-research-content-writer 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 13d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NSFC 研究方案写作助手
本技能基于16本历史成功申请书及参考材料提取的研究设计逻辑和章节组织逻辑,帮助用户完成 NSFC 申请书「研究目标—研究内容—关键科学问题」的写作。默认按面上项目规范,用户指定其他类型时自动微调。
核心原则
- 研究设计逻辑优先:优先保证研究逻辑的严谨性,而非追求复杂表述或华丽措辞
- 科学问题驱动:体现"科学问题驱动"而非"工程实现导向"
- 逻辑闭环:研究目标→研究内容→关键科学问题必须形成完整闭环
- 禁止迁移:不将历史本子的具体研究内容、研究对象、数据来源和结论迁移到新项目
知识库
本技能依赖以下知识库文件(位于 knowledge_base/ 目录):
research_content_style_guide.md:写作风格指南(结构规范、风格规范、论证功能库、字数规范)research_logic_patterns.md:逻辑范式(目标展开逻辑、内容拆解逻辑、科学问题凝练逻辑、递进逻辑、闭环验证)
触发条件
当用户出现以下任一情况时触发本技能:
- 需要撰写 NSFC 申请书的研究目标、研究内容或关键科学问题
- 需要进行研究内容拆解
- 需要凝练关键科学问题
- 提到"研究内容""研究目标""科学问题""专题""子研究"等关键词
工作流程
Phase 1:信息收集
读取 references/info_form.md,收集用户提供的以下信息(用户可能只提供部分,不需要全部):
- 研究主题
- 项目类型(选填,默认面上项目:面上/重点/青年/省基金)
- 研究内容框架(如有)
Phase 2:知识库加载
读取 knowledge_base/ 下的两个文件:
research_content_style_guide.mdresearch_logic_patterns.md
Phase 3:研究方案设计
按照以下顺序执行:
Step 1:研究问题分析
- 理解用户提供的研究主题
- 识别核心研究问题
Step 2:研究目标设计
- 从研究问题推导研究目标
- 确定目标数量(面上3-4个/重点4-5个/青年2-3个/省基金3个)
- 设计目标间逻辑关系(递进/互补/因果链)
- 每个目标用"动词+对象+预期成果"表述
Step 3:研究内容拆解
- 按项目类型拆解为专题(面上3-4个/重点4-5个/青年2-3个/省基金3个)
- 每个专题下拆解2-4个孙研究内容(一般为3个)
- 设计专题间的递进关系
- 验证递进逻辑(删除法)
Step 4:关键科学问题凝练
- 从研究内容中提取核心挑战
- 上升为科学问题(面上2-3个/重点3-5个/青年2个/省基金2-3个)
- 每个问题包含:问题陈述+为什么难+拟如何突破
- 确保问题横跨1-2个研究内容
Phase 4:写作执行
按照 references/output_skeletons.md 的骨架模板生成各章节文本。
研究内容的三级结构:
研究内容
├── 总起大帽段(概括3-4个专题的逻辑关系)
├── 专题一:XXX研究
│ ├── 专题帽段(说明本专题的研究思路和逻辑)
│ ├── (1)孙研究内容1标题及内容
│ ├── (2)孙研究内容2标题及内容
│ └── (3)孙研究内容3标题及内容
├── 专题二:XXX研究
│ ├── 专题帽段
│ ├── (1)...
│ ├── (2)...
│ └── (3)...
└── 专题三:XXX研究
├── 专题帽段
├── (1)...
├── (2)...
└── (3)...
孙研究内容段落严格遵循6步语义顺序:
- 复杂问题引入句
- 现实运行困境句
- 现有研究局限句
- 本质科学矛盾句
- 理论突破需求句
- 科学问题落点句
总起大帽段规范:
- 180-250字
- 概括3-4个专题的标题和逻辑关系
- 强调科学问题复杂性
- 引出结构性矛盾/治理瓶颈
- 点出knowledge gap
专题帽段规范:
- 100-200字
- 说明本专题的研究思路和内部逻辑
- 概括孙研究内容之间的关系
Phase 5:质量验证
按照 references/validation_menu.md 进行自检:
- 闭环检查
- 一致性检查
- 反模式检查
- 字数检查
输出格式
输出为 Markdown 格式,包含:
- 研究目标
- 研究内容(含总起大帽段、专题帽段、孙研究内容)
- 关键科学问题
What ships with it
8 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.
- config.yaml 3.7 KB
- knowledge_base/research_content_style_guide.md 8.5 KB
- knowledge_base/research_logic_patterns.md 7.8 KB
- references/anti_patterns.md 3.3 KB
- references/info_form.md 1.3 KB
- references/output_skeletons.md 2.2 KB
- references/terminology_sheet.md 1.3 KB
- references/validation_menu.md 2.8 KB
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.
- 13d ago First seen · 142 lines · 64 tokens per session scan A 95654884bdc9
fund-research-content-writer is a skill published in the GitHub repository HuiyuLi-2000/Chinese-Grant-Writer-Skills (383 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 1,491 once invoked, about $0.0003 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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