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-plangit 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-plan)<a href="https://agentmods.dev/skills/huangwb8/chineseresearchlatex/research-plan"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-plan/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-plan"><img src="https://agentmods.dev/badge/skills/huangwb8/chineseresearchlatex/research-plan.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 | $0.00074 | $0.03681 |
| Opus 5.5 | $0.00030 | $0.01472 |
| Sonnet 5.5 | $0.00015 | $0.00736 |
| Haiku 4.5 | $0.00007 | $0.00368 |
Grade A, and why
research-plan 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 1mo 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Plan
核心功能
本 Skill 通过系统化调研顶尖期刊/会议论文，为用户制定个性化、可落地的科研分析策略。
核心价值
- 证据驱动：基于顶级期刊论文的方法学，而非凭空设计
- 系统性调研：自动化的文献检索、筛选、下载流程
- 深度学习：从 PDF 中提取分析方法和技术细节
- 可落地性：输出清晰的目标/步骤，可直接执行
使用场景
- 制定实验设计方案
- 规划数据分析流程
- 设计技术路线图
- 确定统计方法选择
- 制定可视化策略
输入规范
必需输入
- 需求描述：清晰描述要解决的问题和目标
可选输入
- 工作目录：默认当前目录
- 文献年份范围：默认近 5 年
- 目标期刊/会议：指定特定来源
- 语言偏好：默认英文
- 最大文献数：默认 30 篇
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
- 因本 skill 设计缺陷导致的 bug，先用
bensz-collect-bugs规范记录到~/.bensz-skills/bugs/，不要直接修改用户本地已安装的 skill 源码；若有 workaround，先记 bug，再继续完成任务。 - 只有用户明确要求“report bensz skills bugs”等公开上报时，才用本地
gh上传新增 bug 到huangwb8/bensz-bugs；不要 pull / clone 整个仓库。
基于文献调研的科研分析策略规划助手
旧名 make-research-plan 仅作为 prompt 兼容别名保留；历史 .make-research-plan/ 仅作显式兼容读取、迁移或清理，新运行统一使用任务级工作区。
阶段 0：初始化
输入：
- 用户需求描述
- 工作目录路径（可选，默认当前目录）
操作：
- 验证工作目录存在且可写
- 创建任务级隐藏工作目录
.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/research-plan/ - 明确告知用户创建的目录位置和用途
输出：
.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/research-plan/目录结构
目录结构：
.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/research-plan/
├── input/papers/ # 下载的 PDF 文献
├── input/metadata/ # 调研元数据
│ ├── theme.json # 主题和关键词
│ └── search_history.json # 检索历史
├── output/extracted/ # 提取的文献信息
│ └── papers_info.json # 论文结构化信息
├── output/analysis-framework.md # 分析框架总结
├── output/plan.md # 任务内草稿
└── log/ # 命令、验证与错误日志
阶段 1：主题提取与文献调研
步骤 1.1：主题提取
目标： 从用户需求中结构化提取研究主题和关键词
方法：
- 分析用户需求文本
- 识别核心研究领域
- 提取关键概念术语
- 生成多个相关主题（如适用）
- 为每个主题生成 3-5 个关键词
输出： metadata/theme.json
{
"primary_topic": {
"name": "主题名称",
"description": "主题描述",
"keywords": ["关键词1", "关键词2", "关键词3"]
},
"secondary_topics": [
{
"name": "相关主题1",
"keywords": ["关键词1", "关键词2"]
}
],
"extracted_at": "2026-01-19T09:30:00Z"
}
步骤 1.2：文献检索
检索策略：
- 多源检索：
- PubMed（生物医学）
- Google Scholar（综合）
- IEEE Xplore（工程技术）
- arXiv（预印本）
- Semantic Scholar（AI 驱动）
What ships with it
11 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 2.5 KB
- config.yaml 1.6 KB
- README.md 4.1 KB
- references/implementation-notes.md 1.6 KB
- references/literature-search-guide.md 3.2 KB
- references/methodology-extraction-patterns.md 2.4 KB
- references/output-templates.md 1.5 KB
- scripts/bibtex.py 4.8 KB runs code
- scripts/initialize.py 3.7 KB runs code
- scripts/utils.py 3.2 KB runs code
- scripts/validate.py 6.8 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.
- 1mo ago Changed · +6 lines d91ccb8a7110
- 1mo ago First seen · 414 lines · 74 tokens per session scan A 6eff973d0881
research-plan is a skill published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,980 stars, last pushed 10d ago), licensed MIT. It adds 74 tokens to every session and 3,681 once invoked, about $0.0003 per session on Opus 5.5. 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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