ChineseResearchLaTeX: Instructions file for Claude Code

CLAUDE.md

ChineseResearchLaTeX CLAUDE.md is an instructions file for Claude Code from huangwb8/ChineseResearchLaTeX. It costs 496 tokens per session, scanned A, original, MIT.

A project guide for a Chinese research LaTeX template collection, where LaTeX is a system for producing structured documents such as academic papers.

In plain words
What is it for?
Use it when editing project instructions, linking files, tracking complex work, recording changes, or preparing a release.
Why use it?
It explains how the general instructions and Claude-specific instructions fit together, reducing duplicated edits and missed project-management requirements.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; names the TodoWrite tool; mentions Claude Code.

This is huangwb8/ChineseResearchLaTeX's own configuration. It tells Claude Code how to work on ChineseResearchLaTeX itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ChineseResearchLaTeX configures →

About the project

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.

huangwb8/ChineseResearchLaTeX · 2,713 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to huangwb8/ChineseResearchLaTeX. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/huangwb8/ChineseResearchLaTeX/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/huangwb8/ChineseResearchLaTeX

Made for: Claude Code.

Wrote 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.

agentmods badge for ChineseResearchLaTeX CLAUDE.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/huangwb8/chineseresearchlatex/claude-md/github.svg)](https://agentmods.dev/instructions/huangwb8/chineseresearchlatex/claude-md)
Your own site
<a href="https://agentmods.dev/instructions/huangwb8/chineseresearchlatex/claude-md"><img src="https://agentmods.dev/badge/instructions/huangwb8/chineseresearchlatex/claude-md/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.

agentmods 80×15 button for ChineseResearchLaTeX CLAUDE.md

Your own site · 80×15
<a href="https://agentmods.dev/instructions/huangwb8/chineseresearchlatex/claude-md"><img src="https://agentmods.dev/badge/instructions/huangwb8/chineseresearchlatex/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 496 This file is loaded in full into every session.
When invoked 496 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00496 $0.00496
Opus 5 $0.00248 $0.00248
Sonnet 5 $0.00099 $0.00099
Haiku 4.5 $0.00050 $0.00050

Measured 10d ago against content hash 042751dcac00, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

ChineseResearchLaTeX CLAUDE.md 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 10d 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

CLAUDE.md · 43 lines

What it actually says

中国科研常用 LaTeX 模板集 - Claude Code 项目指令

核心指令

@./AGENTS.md

Claude Code 特定说明

文件引用规范

在 Claude Code 中引用文件时,使用 markdown 链接语法:

  • 文件[filename.md](路径/filename.md)
  • 特定行[filename.md:42](路径/filename.md#L42)
  • 行范围[filename.md:42-51](路径/filename.md#L42-L51)
  • 目录[目录名/](路径/目录名/)

任务管理

  • 使用 TodoWrite 工具跟踪复杂任务的进度
  • 完成任务后及时标记为 completed
  • 拆分大任务为可管理的小步骤
  • 涉及 Release 发布任务时,将 python scripts/pack_release.py --tag <tag> --upload 视为默认必做检查点;未执行成功前,不要写成“已发布完成”

代码变更规范

  • 修改代码前先使用 Read 工具阅读文件
  • 优先使用 Edit 工具进行精确修改
  • 避免不必要的格式化或重构

与 AGENTS.md 的关系

  • AGENTS.md:跨平台通用项目指令(Single Source of Truth)
  • CLAUDE.md:通过 @./AGENTS.md 自动引用 + Claude Code 特定适配
  • 维护流程
    1. 修改 AGENTS.md(唯一需要手动维护的项目指令文件)
    2. CLAUDE.md 会自动读取最新的 AGENTS.md 内容
    3. 无需运行任何同步命令
  • 参考文档

提示:修改 AGENTS.md 后,请立即在 CHANGELOG.md 中记录变更。这是项目管理的强制性要求,不是可选项。若任务涉及 Release 发布,还需在最终答复中显式说明 python scripts/pack_release.py --tag <tag> --upload 是否已执行成功。

Changes

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.

  1. 10d ago First seen · 43 lines · 496 tokens per session scan A 042751dcac00

Subscribe to this mod's changes

ChineseResearchLaTeX CLAUDE.md is an instructions file published in the GitHub repository huangwb8/ChineseResearchLaTeX (2,713 stars, last pushed 2d ago), licensed MIT. It adds 496 tokens to every session, about $0.0025 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.

Related

Other instructions, from other repositories

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,153 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,469 tokens