everything-claude-code-zh is a Chinese translation of a collection of configurations for Claude Code and other AI coding agents. It provides agents, skills, hooks, commands, rules, and MCP configurations intended to support development workflows such as memory persistence, security scanning, evaluation, and research-first work. The catalogue includes commands, skills, agents, instructions, and a plugin from this configuration set.
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 agentmods add commands/xu-xiang/everything-claude-code-zh/plangit clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zhWrote 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/commands/xu-xiang/everything-claude-code-zh/plan)<a href="https://agentmods.dev/commands/xu-xiang/everything-claude-code-zh/plan"><img src="https://agentmods.dev/badge/commands/xu-xiang/everything-claude-code-zh/plan.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00017 | $0.00412 |
| Opus 5 | $0.00009 | $0.00206 |
| Sonnet 5 | $0.00003 | $0.00082 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
计划命令（Plan Command）
为以下内容创建详细的实施计划：$ARGUMENTS
你的任务（Your Task）
- 重述需求（Restate Requirements） - 明确需要构建的内容
- 识别风险（Identify Risks） - 发现潜在问题、阻塞点和依赖项
- 创建分步计划（Create Step Plan） - 将实施过程分解为若干阶段
- 等待确认（Wait for Confirmation） - 在继续之前必须获得用户批准
输出格式（Output Format）
需求重述（Requirements Restatement）
[对将要构建的内容进行清晰、简洁的重述]
实施阶段（Implementation Phases）
[阶段 1：描述]
- 步骤 1.1
- 步骤 1.2 ...
[阶段 2：描述]
- 步骤 2.1
- 步骤 2.2 ...
依赖项（Dependencies）
[列出所需的外部依赖、API 和服务]
风险（Risks）
- 高（HIGH）：[可能阻碍实施的关键风险]
- 中（MEDIUM）：[需要处理的中度风险]
- 低（LOW）：[次要顾虑]
预计复杂度（Estimated Complexity）
[高/中/低（HIGH/MEDIUM/LOW），并附带时间预估]
等待确认（WAITING FOR CONFIRMATION）：是否执行此计划？(yes/no/modify)
关键（CRITICAL）：在用户明确通过 "yes"、"proceed" 或类似的肯定回复确认之前，不要编写任何代码。
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.
- 5d ago First seen · 50 lines · 17 tokens per session scan A 1122f42545e1
plan is a command published in the GitHub repository xu-xiang/everything-claude-code-zh (1,929 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 412 once invoked, about $0.0001 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.
Other commands, from other repositories
awesome-chatgpt
Search awesome-ChatGPT-repositories for open-source GitHub repositories related to ChatGPT and LLMs.
test
Generate comprehensive tests.
init
Scaffold a new MindBase project (v2 layout). Usage: /mb:init [template] [-- mission ...].
commit
智能生成 Git 提交信息并提交.
pr
Handle the full workflow from current branch state to an open, CI-monitored pull request.
doctor.es
Diagnostica problemas de inferencia LLM en Mac: asiai doctor verifica el estado de los motores, conflictos de puertos, carga de modelos y estado de la GPU.