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 laolaoshiren/claude-code-skills-zh --skill zh-docgengit clone --depth 1 https://github.com/laolaoshiren/claude-code-skills-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/skills/laolaoshiren/claude-code-skills-zh/zh-docgen)<a href="https://agentmods.dev/skills/laolaoshiren/claude-code-skills-zh/zh-docgen"><img src="https://agentmods.dev/badge/skills/laolaoshiren/claude-code-skills-zh/zh-docgen/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/laolaoshiren/claude-code-skills-zh/zh-docgen"><img src="https://agentmods.dev/badge/skills/laolaoshiren/claude-code-skills-zh/zh-docgen.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 | $0.00019 | $0.00284 |
| Opus 5 | $0.00010 | $0.00142 |
| Sonnet 5 | $0.00004 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
zh-docgen 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 12d 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
中文文档生成器
触发条件
当用户要求生成文档、写技术文档、生成 API 文档、项目说明时激活此技能。
工作流程
- 扫描项目结构 — 识别项目类型（Web 应用/CLI 工具/库/服务）
- 分析代码 — 提取模块、函数、类、接口的签名和注释
- 生成文档 — 输出以下文档：
生成内容
- 项目概述 — 中文简介、技术栈、架构图（Mermaid）
- 快速开始 — 安装、配置、运行步骤
- API 参考 — 自动生成的接口文档
- 使用示例 — 常见场景的代码示例
- 常见问题 — FAQ 部分
输出格式
生成一个或多个 Markdown 文件，中文为主，技术术语保留英文。
技巧
- 优先读取已有 README、package.json、pyproject.toml 获取项目元信息
- 分析 import/require 语句推断依赖关系
- 从 docstring/JSDoc 提取已有文档
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.
- 12d ago First seen · 31 lines · 19 tokens per session scan A df873221240f
zh-docgen is a skill published in the GitHub repository laolaoshiren/claude-code-skills-zh (832 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 284 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.
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Run a lightweight, blame-free postmortem after an incident, failed launch, or missed deadline — for one person. Use when the user says "that didn't go well", "the launch flopped", "we had an outage", "I missed my deadline", or wants to learn from a recent failure.
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Convert a vague user complaint into a reproducible bug report a solo dev can act on. Use when the user pastes a confusing customer message, says "user says it's broken but I can't repro", "this email makes no sense", or wants to triage a bug report.
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Generate a user-facing CHANGELOG entry from raw git log output. Use when the user is preparing a release, says "what changed since last version", asks to write release notes, or wants to summarize a batch of commits for end users.
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Run a 5-minute personal standup for a solo dev — what shipped yesterday, what's blocked, what's next today. Use when the user asks for a daily check-in, says "what should I work on today", "what did I do yesterday", or wants to break out of a productivity slump.
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Draft a polite-but-firm reply to a tricky customer email — refund request, scope creep, complaint, billing dispute. Use when the user pastes a customer email and asks for a reply, mentions "how do I respond", "user is asking for a refund", "they want a discount", or any awkward customer-facing communication.