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Agent Skill · 前端设计

debug-agent(调试 Agent)

debug-agent 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

debug-agent 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在前端设计类的 10,216 个 Skill 里排第 7,570。

总安装 364周安装 15GitHub Stars 3,417下载 119核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:debug-agent(调试 Agent)
来源仓库:https://github.com/millionco/expect
仓库路径:skills/debug-agent
安装命令:
npx skills add https://github.com/millionco/expect --skill debug-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/millionco/expect --skill debug-agent

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

debug-agent 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。通过 npx skills add 命令从指定仓库安装,建议查看原始 README 了解调用方式。

使用前请核实权限范围、项目维护状态,并注意是否触发网络访问或系统操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/debug-agent

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/millionco/expect --skill debug-agent;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

debug-agent 怎么安装?

在终端执行 npx skills add https://github.com/millionco/expect --skill debug-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

debug-agent 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

debug-agent 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/millionco/expect --skill debug-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

debug-agent 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

debug-agent 用的人多吗?

公开数据是累计安装 364 次,近一周安装 15 次,下载 119 次,GitHub 3,417 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

debug-agent 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 3,417 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
debug-agent

调试 Agent · 本页

3643,417执行命令未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Debug Mode

You are now in DEBUG MODE. You must debug with runtime evidence.

Why this approach: Traditional AI agents jump to fixes claiming 100% confidence, but fail due to lacking runtime information. They guess based on code alone. You cannot and must NOT fix bugs this way — you need actual runtime data.

Your systematic workflow:

  1. Generate 3-5 precise hypotheses about WHY the bug occurs (be detailed, aim for MORE not fewer)
  2. Instrument code with logs (see Logging section) to test all hypotheses in parallel
  3. Ask user to reproduce the bug. Provide clear, numbered reproduction steps. Remind the user to restart any apps/services if instrumented files are cached or bundled. Ask the user to confirm when done.
  4. Analyze logs: evaluate each hypothesis (CONFIRMED/REJECTED/INCONCLUSIVE) with cited log line evidence
  5. Fix only with 100% confidence and log proof; do NOT remove instrumentation yet
  6. Verify with logs: ask user to run again, compare before/after logs with cited entries
  7. If logs prove success and user confirms: remove logs and explain. If failed: FIRST remove any code changes from rejected hypotheses (keep only instrumentation and proven fixes), THEN generate NEW hypotheses from different subsystems and add more instrumentation
  8. After confirmed success: explain the problem and provide a concise summary of the fix (1-2 lines)

Critical constraints:

  • NEVER fix without runtime evidence first
  • ALWAYS rely on runtime information + code (never code alone)
  • Do NOT remove instrumentation before post-fix verification logs prove success and user confirms that there are no more issues
  • Fixes often fail; iteration is expected and preferred. Taking longer with more data yields better, more precise fixes

Logging

STEP 0: Start the logging server (MANDATORY BEFORE ANY INSTRUMENTATION)

CRITICAL: The server is a long-running process. You MUST run it in the BACKGROUND.

Run the debug server as a background process before any instrumentation. The server stays running for the entire debug session — it is NOT a one-shot command.

npx debug-agent 2>&1 &

YOU MUST BACKGROUND THIS COMMAND. Do NOT run it in the foreground. Do NOT wait for it to complete — it never completes, it is a persistent server. Use & (shell background), nohup, or your agent's background/async command execution. If your agent platform supports block_until_ms: 0 or equivalent, use that. If it supports running commands in a separate terminal, do that. The command MUST NOT block your workflow.

The server prints a single JSON line to stdout on startup:

{
  "sessionId": "a1b2c3",
  "port": 54321,
  "endpoint": "http://127.0.0.1:54321/ingest/a1b2c3",
  "logPath": "/tmp/debug-agent/debug-a1b2c3.log"
}

Capture and remember these values:

  • Server endpoint: The endpoint value (the HTTP endpoint URL where logs will be sent via POST requests)
  • Log path: The logPath value (NDJSON logs are written here)
  • Session ID: The sessionId value (unique identifier for this debug session)

If the server fails to start, STOP IMMEDIATELY and inform the user.

  • DO NOT PROCEED with instrumentation without valid logging configuration.
  • The server is idempotent — if one is already running, it returns the existing server's info instead of starting a duplicate.
  • You do not need to pre-create the log file; it will be created automatically when your instrumentation first writes to it.

STEP 1: Understand the log format

  • Logs are written in NDJSON format (one JSON object per line) to the file specified by the log path.
  • For JavaScript/TypeScript, logs are sent via a POST request to the server endpoint during runtime, and the logging server writes these as NDJSON lines to the log path file.
  • For other languages (Python, Go, Rust, Java, C/C++, Ruby, etc.), you should prefer writing logs directly by appending NDJSON lines to the log path using the language's standard library file I/O.

Example log entry:

{
  "sessionId": "a1b2c3",
  "id": "log_1733456789_abc",
  "timestamp": 1733456789000,
  "location": "test.js:42",
  "message": "User score",
  "data": { "userId": 5, "score": 85 },
  "runId": "run1",
  "hypothesisId": "A"
}

STEP 2: Insert instrumentation logs

  • In JavaScript/TypeScript files, use this one-line fetch template (replace ENDPOINT and SESSION_ID with values from Step 0), even if filesystem access is available:
fetch('ENDPOINT',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({sessionId:'SESSION_ID',location:'file.js:LINE',message:'desc',data:{k:v},timestamp:Date.now()})}).catch(()=>{});
  • In non-JavaScript languages (Python, Go, Rust, Java, C, C++, Ruby), instrument by opening the log path in append mode using standard library file I/O, writing a single NDJSON line with your payload, and then closing the file. Keep these snippets as tiny and compact as possible (ideally one line, or just a few).
  • Decide how many instrumentation logs to insert based on the complexity of the code under investigation and the hypotheses you are testing. A single well-placed log may be enough when the issue is highly localized; complex multi-step flows may need more. Aim for the minimum number that can confirm or reject ALL your hypotheses. Guidelines:

- At least 1 log is required; never skip instrumentation entirely - Do not exceed 10 logs — if you think you need more, narrow your hypotheses first - Typical range is 2-6 logs, but use your judgment

  • Choose log placements from these categories as relevant to your hypotheses:

- Function entry with parameters - Function exit with return values - Values BEFORE critical operations - Values AFTER critical operations - Branch execution paths (which if/else executed) - Suspected error/edge case values - State mutations and intermediate values

  • Each log must map to at least one hypothesis (include hypothesisId in payload).
  • Use this payload structure: {sessionId, runId, hypothesisId, location, message, data, timestamp}
  • REQUIRED: Wrap EACH debug log in a collapsible code region:

- Use language-appropriate region syntax (e.g., // #region debug log, // #endregion for JS/TS) - This keeps the editor clean by auto-folding debug instrumentation

  • FORBIDDEN: Logging secrets (tokens, passwords, API keys, PII)

STEP 3: Clear previous log file before each run (MANDATORY)

  • Delete the file at the log path before asking the user to run.
  • If deleting is unavailable or fails: instruct user to manually delete the log file.
  • This ensures clean logs for the new run without mixing old and new data.
  • Do NOT use shell commands (rm, touch, etc.); use the delete_file tool only.
  • Clearing the log file is NOT the same as removing instrumentation; do not remove any debug logs from code here.
  • CRITICAL: Only delete YOUR log file (the one at the log path from Step 0). NEVER delete, modify, or overwrite log files belonging to other debug sessions. Other sessions may have log files in the same directory with different session IDs in their filenames — leave them untouched.

STEP 4: Read logs after user runs the program

  • After the user runs the program and confirms completion in their interface, do NOT ask them to type "done"; then read the file at the log path.
  • The log file will contain NDJSON entries (one JSON object per line) from your instrumentation.
  • Analyze these logs to evaluate your hypotheses and identify the root cause.
  • If log file is empty or missing: tell user the reproduction may have failed and ask them to try again.

STEP 5: Keep logs during fixes

  • When implementing a fix, DO NOT remove debug logs yet.
  • Logs MUST remain active for verification runs.
  • You may tag logs with runId="post-fix" to distinguish verification runs from initial debugging runs.
  • FORBIDDEN: Removing or modifying any previously added logs in any files before post-fix verification logs are analyzed or the user explicitly confirms success.
  • Only remove logs after a successful post-fix verification run (log-based proof) or explicit user request to remove.

Critical Reminders (must follow)

  • Keep instrumentation active during fixes; do not remove or modify logs until verification succeeds or the user explicitly confirms.
  • FORBIDDEN: Using setTimeout, sleep, or artificial delays as a "fix"; use proper reactivity/events/lifecycles.
  • FORBIDDEN: Removing instrumentation before analyzing post-fix verification logs or receiving explicit user confirmation.
  • Verification requires before/after log comparison with cited log lines; do not claim success without log proof.
  • Do not create the log file manually; it's created automatically.
  • Clearing the log file is not removing instrumentation.
  • NEVER delete or modify log files that do not belong to this session. Only touch the log file at the exact path from Step 0.
  • Always try to rely on generating new hypotheses and using evidence from the logs to provide fixes.
  • If all hypotheses are rejected, you MUST generate more and add more instrumentation accordingly.
  • Remove code changes from rejected hypotheses: When logs prove a hypothesis wrong, revert the code changes made for that hypothesis. Do not let defensive guards, speculative fixes, or unproven changes accumulate. Only keep modifications that are supported by runtime evidence.
  • Prefer reusing existing architecture, patterns, and utilities; avoid overengineering. Make fixes precise, targeted, and as small as possible while maximizing impact.

Server API reference

MethodEffect
POST /ingest/:sessionIdAppend JSON body as NDJSON line to log file
GET /ingest/:sessionIdRead full log file contents
DELETE /ingest/:sessionIdClear the log file

平台分布

Codex

33.05%
按下载量换算39

Claude

30.71%
按下载量换算37

Cursor

18.84%
按下载量换算22

Gemini CLI

9.54%
按下载量换算11

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/millionco/expect --skill debug-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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