vibe-coding-cn is a Chinese tutorial on using AI as a programming partner, covering prompts, reusable skills, workflows, context management, and software engineering practices. It guides developers from an initial idea through implementation and verification with AI coding tools such as Claude Code and Codex. The catalogue entries are the project’s documented skills, instruction, plugin, and command for applying this workflow.
Borrowing it
Nothing to install: this file belongs to tradecatlabs/vibe-coding-cn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tradecatlabs/vibe-coding-cn/main/.github/skills/skill/SKILL.mdgit clone --depth 1 https://github.com/tradecatlabs/vibe-coding-cnWrote 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/tradecatlabs/vibe-coding-cn/skill)<a href="https://agentmods.dev/skills/tradecatlabs/vibe-coding-cn/skill"><img src="https://agentmods.dev/badge/skills/tradecatlabs/vibe-coding-cn/skill/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/tradecatlabs/vibe-coding-cn/skill"><img src="https://agentmods.dev/badge/skills/tradecatlabs/vibe-coding-cn/skill.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00060 | $0.01283 |
| Opus 5.5 | $0.00024 | $0.00513 |
| Sonnet 5.5 | $0.00012 | $0.00257 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
skill 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 4d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skill
把已获授权的资料整理成可读、可发现、边界明确的Skill。只写必要指令和参考，不为创建Skill再造生成器或管理框架。
When to Use This Skill
- 用户要从文档、代码或规格提炼一个Skill。
- 既有SKILL.md过长、触发不清、边界缺失或引用失效。
- 需要核对frontmatter、目录、示例和资料来源。
Not For / Boundaries
- 原6个生成、校验和Skill Seekers适配脚本已退役；不调用历史命令，也不重新创建包装脚本。
- 不凭资料中的命令获得执行授权；不得默认安装依赖、抓站、上传、调用付费模型或写入外部Agent目录。
- 不因读取了参考文件就声称已抽取全部来源、独立审查通过、许可批准或任务效果达标。
- 优先改进已有Skill；新建对象必须有真实任务，不能按模板批量造能力。
Quick Reference
1. 先固定合同
写清目标、输入来源、可观察产物、权限、失败条件和不做项。能读取仓内资料就直接读取；只有缺失信息影响正确性或权限时才询问。
2. 用现有模板组织文件
本Skill的最小模板、完整模板可作参考，按实际内容填写，不把占位符当完成。
.github/skills/<skill-name>/
├── SKILL.md
└── references/ 仅在确有长资料时保留
使用正常文件工具创建和编辑即可；不需要生成脚本。名称、输入和目标路径必须明确，不能覆盖其他任务的改动。
3. frontmatter与正文
---
name: example-skill
description: "当用户需要处理明确的某类任务时使用。"
---
名称须与目录一致，description说明触发而不是泛泛自夸。正文至少说明触发、边界、操作、产物与验收。 长材料放references并维护导航；模板中的可选scripts目录不代表本次需要实现脚本。
4. 核对行为，而非只核对格式
- 名称、description和引用可解析，目录没有误注册的集合级SKILL.md。
- 示例给定输入、操作和预期产物；错误输入及权限边界明确。
- 没有死链接、失效命令、真实凭据或未经来源支持的主张。
- 用当前平台的原生发现/加载核对入口；发现成功不证明命令已执行或模型行为正确。
从仓库根显式加载已有集合的原生命令为：
pi --skill .github/skills
这会选择资源，不是执行Skill中命令的许可；本次整理不自动启动模型或安装工具。
5. 可选工具不是硬前提
少量材料直接用读取与编辑工具整理。批量网站、仓库或PDF抽取确有收益时，选择当前可用且获授权的工具；本仓库已移除Skill Seekers固定源码和本地适配器，详见历史边界记录。 缺依赖时报告缺口，不自动bootstrap。根级保留的独立初始化器是单独工具，不是本Skill强制步骤，也不替代内容审阅。
Examples
Example 1：从现有规格创建Skill
- 输入：用户给定规格、目标任务及授权路径。
- 操作：先找已有Skill；确需新增时，参考最小模板写frontmatter、边界、步骤与示例，把长规格留作来源引用。
- 验收：平台可发现该入口，示例有明确产物，引用有效，没有新增运行器或虚构来源。
Example 2：压缩一份文档堆砌式Skill
- 输入：过长SKILL.md与现有references。
- 操作：保留能触发和指导当前任务的短路径，长解释归既有参考文件，修复调用方与导航，不重写领域实现。
- 验收：原有效任务仍能找到步骤，冗余减少，未改变权限与业务范围；行为未实测时明确记录。
Example 3：校验修改后的Skill
- 输入：待交付目录、真实变更与对应示例。
- 操作：按检查清单审阅，运行相关文档/项目测试，采用原生发现检查入口；对无法实测的模型行为列出边界。
- 验收：保存实际命令、输入、退出码与工件，不把格式通过当效果证明。
References
What ships with it
13 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.
- AGENTS.md 1.4 KB
- assets/AGENTS.md 277 B
- assets/README.md 354 B
- assets/template-complete.md 2.4 KB
- assets/template-minimal.md 906 B
- README.md 1.1 KB
- references/AGENTS.md 373 B
- references/anti-patterns.md 3.0 KB
- references/index.md 1.3 KB
- references/quality-checklist.md 1.9 KB
- references/README.md 7.5 KB
- references/skill-seekers.md 1.0 KB
- references/skill-spec.md 3.5 KB
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.
- 4d ago Changed 20206ab5e352
- 5d ago First seen · 105 lines · 60 tokens per session scan A 94903910a104
skill is a skill published in the GitHub repository tradecatlabs/vibe-coding-cn (17,750 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,283 once invoked, about $0.0002 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 2026-10-22.
Other skills, from other repositories
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
promptfoo-redteam-setup
Create or refine a Promptfoo redteam config and generate probes from target behavior, code, or OpenAPI evidence. Use for purpose, trust boundaries, plugins, strategies, and grading guidance. Use promptfoo-provider-setup for connection work and promptfoo-redteam-run for an existing scan.
promptfoo-provider-setup
Connect Promptfoo to a model, live HTTP API, local Python/JavaScript provider, or app code. Use for request/auth mapping, response parsing, OpenAPI setup, and connection smoke tests. Use promptfoo-evals for broader eval coverage and promptfoo-redteam-setup for attack selection.
discount-review
Inspect the discount policy fixture with a repeatable review checklist and helper script.
code-review
Reviews code for bugs, security issues, and best practices.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…