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 research-topic-extractorgit 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/research-topic-extractor)<a href="https://agentmods.dev/skills/huangwb8/chineseresearchlatex/research-topic-extractor"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-topic-extractor/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/research-topic-extractor"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-topic-extractor.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 | $0.00109 | $0.01419 |
| Opus 5 | $0.00055 | $0.00709 |
| Sonnet 5 | $0.00022 | $0.00284 |
| Haiku 4.5 | $0.00011 | $0.00142 |
Grade A, and why
research-topic-extractor 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 2d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Topic Extractor
- 从文件、图片、网页、文件夹或自然语言描述中提取结构化综述主题。
- 输出直接服务
research-literature-review或其他文献综述工作流。 - 兼容旧名
get-review-theme的 prompt 触发；系统级旧目录由安装器清理。 - 最高原则：主题要可操作、关键词要能检索、核心问题要具体。
输入
必需：
{输入源}：文件路径、URL、文件夹路径、图片路径，或直接文本描述
可选：
{输出格式}：text/yaml/json，默认text
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。
识别输入类型
- 自然语言描述
- 图片
- URL
- 文本文件
- Word
- 文件夹
提取内容
- 自然语言：直接使用
- 图片：依赖 LLM 原生视觉能力
- URL：优先网页读取工具，失败则请用户提供正文
- 文本 / PDF / Word：直接读取
- 文件夹：递归扫描并合并
.md/.txt/.pdf等核心材料
原则：
- 优先用宿主原生能力和现有标准工具
- 工具不可用时优雅降级，不额外引入脚本依赖
语义提取
围绕以下任务输出：
- 用一句话概括主题
- 提取 5-10 个英文标准术语
- 提取 2-5 个具体研究问题或挑战
格式化
-
text：适合直接复制给下游 skill -
yaml/json：适合结构化衔接 -
topic可直接喂给research-literature-review -
keywords可补充检索策略 -
core_questions可作为综述边界和纳排参考
输出
始终包含三项：
主题关键词核心问题
格式由用户选择：
textyamljson
输出管理
本 Skill 的新任务中间文件统一写入 ./.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/{skill名}/input|output|log/。同一任务复用一个任务根目录；多 Skill 协作才创建 shared/。正式交付物不写入该目录，历史隐藏目录只允许显式兼容读取、迁移或清理。
校验
- 主题要包含研究对象与核心问题或方法
- 关键词优先用标准检索术语
- 核心问题必须具体，避免“意义重大/挑战很多”这种空话
失败与恢复
- 文件不存在：提示用户改路径或直接粘贴内容
- 格式不支持：提示转换
- 内容提取失败：让用户手动提供文本
- URL 解析失败：让用户复制网页正文或提供 PDF
- 图片语义不清：请用户补一句描述
约束
遵守以下公共约束，并执行本 Skill 的专属边界。
公共硬约束
- 任务需要落盘时，使用唯一的
./.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/根目录；共享材料放入shared/，Skill 专属材料放入该 Skill 的input/、output/、log/。 - 正式交付物、源代码和正式计划按项目约定保存，不写入任务工作区；未经授权不覆盖、删除、迁移或远程写入。
- 项目维护变更检查 BAC 可用性并记录需求、AI 产出、工具结果、文件改动和验证摘要；BAC 只做过程审计，不替代署名、责任或合规判断。
- 不记录 API Key、访问令牌、密码、Cookie、环境/凭据文件、私有 Prompt、身份信息、本地用户名、主机名或不必要的大体积原始数据。
- 文件路径必须规范化并限制在授权项目范围内；外部 URL、子进程和网络访问遵循最小权限，防止路径遍历、SSRF 和命令注入。
- Skill 版本唯一记录在自身
config.yaml:skill_info.version；公开 API、协议、目录或配置变更同步文档与CHANGELOG.md。 - 仅将 Skill 或 Bensz 基础设施本身的设计缺陷交给
bensz-collect-bugs；先脱敏写入~/.bensz-skills/bugs/，当前任务不中断，只有用户明确要求才公开上报，禁止直接修改用户已安装的 Skill 源码。
What ships with it
4 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.
- 2d ago Changed 37c5b57509a1
- 11d ago First seen · 126 lines · 109 tokens per session scan A 7dde88a7c720
research-topic-extractor is a skill published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,721 stars, last pushed 3d ago), licensed MIT. It adds 109 tokens to every session and 1,419 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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