External System Debugging

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doccker

external-system-debugging

涉及浏览器、编辑器、CDN/WAF、IM 平台、操作系统剪贴板、第三方 SaaS 等"外部黑盒系统"的代码编写或 bug 调试时触发。强制先抓真实环境数据再推理,避免连续 2 轮"凭代码推理"的修复 no-op。关键词:粘贴/复制异常、跨平台显示不一致、第三方 API 怪结果、CDN/WAF 拦截、本地复现失败、HTML→MD 转换丢属性。

Overview

Publisher	doccker
Repository	`cc-use-exp`
Skill name	`external-system-debugging`
Stars	1.1K
Forks	116
Bundled files	4
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by doccker on GitHub. Read the source before you install it.

Installation

Install the External System Debugging AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/doccker/cc-use-exp/tree/main/.claude/skills/external-system-debugging
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/doccker/cc-use-exp/tree/main/.claude/skills/external-system-debugging

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/doccker/cc-use-exp.git /tmp/cc-use-exp
mkdir -p .claude/skills
cp -r /tmp/cc-use-exp/.claude/skills/external-system-debugging .claude/skills/external-system-debugging

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable External System Debugging in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads External System Debugging on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so External System Debugging is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

外部黑盒系统调试方法论

"代码没说谎,但黑盒系统的行为不在代码里。"

涉及代码以外的环境(浏览器、编辑器、CDN、IM 平台、第三方 API),编码和调试都必须先抓真实数据,再做推理。

何时触发

写代码场景

输出会被第三方平台消费的内容(HTML 推送到 CSDN/微信/Notion/钉钉/飞书)
集成浏览器/操作系统 API(Clipboard / Drag-Drop / File System)
调用经过 CDN/WAF/反代的第三方 HTTP API
在 IM / 富文本编辑器中渲染内容

调试场景

跨浏览器/跨设备/跨终端表现不一致
粘贴/复制行为异常
第三方 API 返回怪结果(body 空、headers 不全、200 但 error)
本地复现失败但线上有问题
连续 2 轮"凭代码推理"的修复都 no-op(最强信号)

黑盒判定清单

维度	不是黑盒	是黑盒(触发本 skill)
控制权	自己写的代码	第三方/平台/浏览器实现
文档完备性	有完整 API 文档	文档不全或有未文档化行为
可调试性	能加日志、断点	只能从外部观察输入输出
行为可预测	给定输入必然得到给定输出	同一输入在不同环境表现不同

满足任意 2 项 → 走本 skill 方法论。

数据采集模板(核心)

不同黑盒类型,第一步该让用户抓什么:

黑盒类型	优先采集	工具/方法
浏览器粘贴/复制	实际剪贴板 HTML	`navigator.clipboard.read()` 列出所有 mime + 打印 outerHTML
第三方编辑器渲染	粘贴前 vs 粘贴后 DOM	DevTools Elements 拷 outerHTML 做 diff
跨浏览器渲染	真机截图 + DOM 结构	真机/BrowserStack;不仅看效果,要拷 DOM
HTTP 第三方 API	完整 request/response	`curl -v` / Charles / mitmproxy;比对 GET vs POST
CDN/WAF 拦截	中间节点信息	`curl -I` 看 `Server` / `Via` 头;空 body + 极简 headers 强信号
IM 平台(微信/钉钉/飞书)	发送+接收两端实际渲染	必须真机截图;不能只看发送端
操作系统剪贴板	所有可用 mime type	`navigator.clipboard.read()` / macOS `pbpaste -Prefer html`
移动端 webview	UA + 实际加载的 CSS/JS	Chrome DevTools 远程调试

⚠️ 抓数据必须保留原始格式:HTML 用 outerHTML 而非 innerText,JSON 用原始字节,二进制用 hexdump。

调试决策流


Bug 现象 → 涉及外部黑盒? 
  ├─ 是 → 数据采集模板 → 基于数据做假设 → 单变量验证 → 修复 → 追加到 references/
  └─ 否 → 走通用 systematic-debugging

关键节点:

不要跳过数据采集:用户描述的现象可能不准确,真实数据才是事实
假设必须基于数据:每个假设都要能引用具体的抓取结果,不能"我以为"
单变量验证:一次只改一个变量,验证假设
修复后必须沉淀:追加新案例到 references/case-{name}.md

反模式警示(铁律)

❌ 反模式	✅ 正确做法
"我以为浏览器会保留 X 属性"	"实测这个浏览器对 X 属性的行为:[抓出来的数据]"
连续 2 轮"凭代码推理"修复都 no-op,第 3 轮继续推理	第 3 轮必须停下,让用户输出真实数据
用 try-catch / 全局 polyfill 兜底掩盖未理解的行为	先理解黑盒实际行为,再决定该不该兜底
多个变量同时改尝试碰运气	单变量验证,有据可循
修好就完,案例只在脑里	追加 `references/case-{name}.md`,标题含系统名+症状
凭"主流编辑器/浏览器应该都支持 X"	列出实测过的版本,未测试的标 unknown

与其他 skill 的边界

skill	何时用	与本 skill 关系
`superpowers:systematic-debugging`	通用 bug 调试	上游通用方法论;本 skill 在其之上加"先抓数据"硬规则
`api-design-safety`	自己设计 API 时	主动设计;本 skill 是被动适配第三方

案例库

修复后追加新案例到 references/,命名约定:

前缀	用途	何时用
`case-{system}-{symptom}.md`	真实案例:已发生过,含具体根因/修复/教训	踩过的坑沉淀
`template-{system}-{symptom}.md`	占位框架:未来该类系统遇到 bug 时填入的模板	预留方向、采集模板预填

template- 文件踩坑填完后请改名为 case-,保持语义清晰。

当前已有:

CSDN paste 列表 bug — HTML→MD 丢 start 属性导致 4 个 "1." 和巨大间距
HTTP CDN/WAF 拦截 — body 空 + headers 极简,GET 正常 POST 异常
代理关键词子串匹配误判 — 正常长文章含 Cloudflare 关键词被代理误判为伪成功错误,强/弱特征拆分修复
浏览器粘贴行为差异(template) — 待补充

规则溯源


> 📋 本回复遵循:`external-system-debugging` - [章节]

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the External System Debugging AI skill do?

Why use External System Debugging on TypingMind?

Because you install it once and use it with any model. External System Debugging is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install External System Debugging in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/doccker/cc-use-exp/tree/main/.claude/skills/external-system-debugging. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use External System Debugging?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with External System Debugging?

As many as you like. As long as a model supports skills, you can use External System Debugging with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the External System Debugging AI skill free?

It is published on GitHub by doccker. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

外部黑盒系统调试方法论

何时触发

黑盒判定清单

数据采集模板(核心)

调试决策流

反模式警示(铁律)

与其他 skill 的边界

案例库

规则溯源

Bundled files

Frequently asked questions

Related AI Skills

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