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debug-agent调试 Agent

Agent Skill

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。

总安装

364

周安装

15

GitHub Stars

3,417

下载量

119
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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