ChineseResearchLaTeX is a collection of LaTeX templates and an AI-assisted workflow for preparing Chinese research documents such as grant proposals, papers, theses, and academic CVs. Researchers use it to plan, format, review, compile, and revise these documents with human oversight. The catalogue skills and instructions support its agent-based research-writing workflow.
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 huangwb8/ChineseResearchLaTeX --skill nsfc-ref-alignmentgit clone --depth 1 https://github.com/huangwb8/ChineseResearchLaTeXWrote 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/huangwb8/chineseresearchlatex/nsfc-ref-alignment)<a href="https://agentmods.dev/skills/huangwb8/chineseresearchlatex/nsfc-ref-alignment"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/nsfc-ref-alignment/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/huangwb8/chineseresearchlatex/nsfc-ref-alignment"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/nsfc-ref-alignment.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 | $0.00090 | $0.02577 |
| Opus 5 | $0.00045 | $0.01288 |
| Sonnet 5 | $0.00018 | $0.00515 |
| Haiku 4.5 | $0.00009 | $0.00258 |
Grade A, and why
nsfc-ref-alignment 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 today.
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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NSFC Ref Alignment
适用场景
- 你有一份 NSFC 标书(LaTeX 项目),希望检查:
.tex里所有\cite{...}/\citep{...}/\citet{...}等引用的 bibkey 是否都存在于.bib.bib条目的基础字段是否完备、格式是否明显错误(如 DOI 非法、年份缺失)- 引用所在句子的“语义主张”是否与该文献(至少从 title/author/year/venue/DOI 等元信息)相匹配,是否存在乱引/过度主张/张冠李戴风险
- 你希望只得到一份报告先人工审核(改
.bib/ 改正文属于大事)。
不适用
- 你希望“自动替换 bibkey / 自动改写正文”——本 skill 默认禁止直接修改(除非你明确要求)。
- 你只是想补齐 BibTeX 条目:优先使用
nsfc-bib-manager。
输入
project_root:标书项目根目录(如projects/NSFC_General)- 可选:
main_tex:主入口 tex(默认main.tex) - 可选:
report_dir:报告输出目录(默认./references,相对你运行 skill 的当前目录) - 可选:
verify_online:是否进行在线核验(默认 false;仅做确定性查询,失败降级)
流程
输入
按用户请求和配置文件提供必要输入;缺失信息应明确列出并停止依赖该输入的步骤。
执行步骤
- 当用户环境中出现因本 skill 设计缺陷导致的 bug 时,优先使用
bensz-collect-bugs按规范记录到~/.bensz-skills/bugs/,严禁直接修改用户本地 Claude Code / Codex 中已安装的 skill 源码。 - 若 AI 仍可通过 workaround 继续完成用户任务,应先记录 bug,再继续完成当前任务。
- 当用户明确要求“report bensz skills bugs”等公开上报动作时,调用本地
gh与bensz-collect-bugs,仅上传新增 bug 到huangwb8/bensz-bugs;不要 pull / clone 整个 bug 仓库。
步骤 1:预检与定位
- 确认
project_root存在且包含main_tex。 - 自动解析
main_tex的\input{}/\include{}依赖树,收集所有涉及的.tex文件。 - 自动发现
\bibliography{...}/\addbibresource{...}指向的.bib文件;若发现 0 个.bib,则回退为在project_root下搜索*.bib并给出 warning。
步骤 2:确定性抽取(脚本执行)
运行脚本生成结构化输入与确定性报告:
cd /path/to/ChineseResearchLaTeX
python3 skills/nsfc-ref-alignment/scripts/run_ref_alignment.py \
--project-root "projects/NSFC_General" \
--main-tex "main.tex" \
--report-dir "references" \
--prepare
如需在线核验(建议只对最终稿/重点条目开启):
python3 skills/nsfc-ref-alignment/scripts/run_ref_alignment.py \
--project-root "projects/NSFC_General" \
--main-tex "main.tex" \
--report-dir "references" \
--prepare \
--verify-online
步骤 3:宿主 AI 语义核查(本 skill 的核心)
宿主 AI 在读取 {run_dir}/ai_ref_alignment_input.json 后,逐条核查:
-
真实性/存在性(P0)
- bibkey 缺失:正文引用了不存在的条目
- DOI/URL 明显无效或在线核验失败且元信息严重不一致
-
语义不匹配风险(P0/P1)
- P0:句子对文献作出了“强断言”,但从元信息看高度不可能(如年份矛盾、领域完全不相关、明显张冠李戴)
- P1:可疑但证据不足(元信息不足、缺少 DOI/缺少 title/作者信息不全)
What ships with it
9 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.
- CHANGELOG.md 1.2 KB
- config.yaml 2.5 KB
- README.md 5.6 KB
- scripts/bib_utils.py 5.3 KB runs code
- scripts/latex_scanner.py 8.8 KB runs code
- scripts/online_verify.py 4.7 KB runs code
- scripts/report_utils.py 4.9 KB runs code
- scripts/run_ref_alignment.py 14 KB runs code
- scripts/runtime_utils.py 2.8 KB runs code
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.
- today Changed · +14 lines 823f04a8e104
- 9d ago First seen · 141 lines · 90 tokens per session scan A 29d5fb2fe4de
nsfc-ref-alignment is a skill published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,711 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 2,577 once invoked, about $0.0005 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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