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Agent Skill · 研究检索

diagnostic-issue-resolver(diagnostic issue resolver 搜索)

用于围绕 GitHub 仓库、Issue、Pull Request、分支、提交和代码协作流程提供辅助能力。它适合让 Agent 查询项目状态、整理变更、辅助创建或检查协作事项,并把仓库中的信息转成可执行的下一步。使用时需要区分只读查询和写入操作;涉及创建 PR、修改 Issue、推送分支或访问私有仓库时,应确认 token 权限、目标仓库范围和用户授权。

diagnostic-issue-resolver 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 38,161。

总安装 1,606周安装 69GitHub Stars 38下载 563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:diagnostic-issue-resolver(diagnostic issue resolver 搜索)
来源仓库:https://github.com/terrylica/cc-skills
仓库路径:skills/diagnostic-issue-resolver
安装命令:
npx skills add https://github.com/terrylica/cc-skills --skill diagnostic-issue-resolver
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/terrylica/cc-skills --skill diagnostic-issue-resolver

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

diagnostic-issue-resolver 用于诊断和修复 TTS 与 Telegram bot 相关的常见问题,适合处理音频播放异常、机器人无响应等场景。适用于 macOS Apple Silicon 环境下的语音合成与聊天机器人集成问题排查。通过收集症状、自动诊断和定向修复来提升问题解决效率。

使用时需注意区分只读查询与写入操作,涉及私有仓库时应确认 token 权限和用户授权。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/diagnostic-issue-resolver

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/terrylica/cc-skills --skill diagnostic-issue-r…;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

    它需要需要联网,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

diagnostic-issue-resolver 怎么安装?

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

diagnostic-issue-resolver 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。

diagnostic-issue-resolver 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

diagnostic-issue-resolver 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。

diagnostic-issue-resolver 用的人多吗?

公开数据是累计安装 1,606 次,近一周安装 69 次,下载 563 次,GitHub 38 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。

diagnostic-issue-resolver 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。

先想清楚的地方

  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
diagnostic-issue-resolver

diagnostic issue resolver 搜索 · 本页

1,60638需要联网未明确有提醒

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

Diagnostic Issue Resolver

Diagnose and fix common TTS + Telegram bot issues through systematic symptom collection, automated diagnostics, and targeted fixes.

Platform: macOS (Apple Silicon)

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

When to Use This Skill

  • TTS audio is not playing or sounds wrong
  • Telegram bot is not responding to messages
  • Kokoro engine errors or timeouts
  • Lock file appears stuck
  • Audio plays twice (race condition)
  • MLX Metal acceleration is not working
  • Queue appears full or backed up

Requirements

  • Access to ~/.claude/automation/claude-telegram-sync/ (bot source)
  • Access to ~/.local/share/kokoro/ (Kokoro engine)
  • Access to ~/.local/state/launchd-logs/telegram-bot/ (launchd logs)
  • Access to ~/.claude/automation/claude-telegram-sync/logs/audit/ (NDJSON audit)

Known Issue Table

IssueLikely CauseDiagnosticFix
No audio outputStale TTS lockstat /tmp/kokoro-tts.lockrm -f /tmp/kokoro-tts.lock
Bot not respondingProcess crashedpgrep -la 'bun.*src/main.ts'Restart: cd ~/.claude/automation/claude-telegram-sync && bun --watch run src/main.ts
Kokoro timeoutFirst-run model loadCheck ~/.cache/huggingface/Wait for download, or re-run kokoro-install.sh --install
Queue fullRapid-fire notificationsCheck queue depth in audit logIncrease TTS_MAX_QUEUE_DEPTH in mise.toml or drain queue
Lock stuck foreverHeartbeat process diedstat /tmp/kokoro-tts.lock + pgrep -x afplayIf lock stale >30s AND no audio process, rm lock
Slow MLX accelerationWrong Python or depspython -c "from mlx_audio.tts.utils import load_model; print('MLX OK')"Reinstall via kokoro-install.sh --upgrade
Double audio playbackLock race conditionCheck for multiple afplay processesKill all: pkill -x afplay, then restart

Workflow Phases

Phase 1: Symptom Collection

Use AskUserQuestion to understand what the user is experiencing. Key questions:

  • What happened? (no audio, wrong audio, bot silent, error message)
  • When did it start? (after upgrade, suddenly, always)
  • What were you doing? (clipboard read, Telegram notification, manual TTS)

Phase 2: Automated Diagnostics

Based on symptoms, run the relevant subset of these checks:

# Lock state
ls -la /tmp/kokoro-tts.lock 2>/dev/null && stat -f "%Sm" /tmp/kokoro-tts.lock || echo "No lock file"

# Audio processes
pgrep -la afplay; pgrep -la say

# Bot process
pgrep -la 'bun.*src/main.ts'

# Kokoro health
~/.local/share/kokoro/.venv/bin/python -c "from mlx_audio.tts.utils import load_model; print('MLX-Audio OK')"

# Recent errors in audit log
tail -20 ~/.claude/automation/claude-telegram-sync/logs/audit/*.ndjson 2>/dev/null | grep -i error

# Recent bot console output
tail -50 /private/tmp/telegram-bot.log 2>/dev/null | grep -i -E '(error|fail|timeout)'

Phase 3: Root Cause Analysis

Map diagnostic output to the Known Issue Table above. Common patterns:

  • Lock file exists + mtime > 30s ago + no afplay = stale lock
  • No bot PID found = bot crashed
  • from mlx_audio.tts.utils import load_model fails = MLX-Audio broken
  • Multiple afplay PIDs = race condition

Phase 4: Fix Application

Apply the targeted fix from the Known Issue Table. Always use the least disruptive fix first.

Phase 5: Verification

After applying the fix, verify the issue is resolved:

# Quick TTS test
~/.local/share/kokoro/.venv/bin/python ~/.local/share/kokoro/tts_generate.py \
  --text "Diagnostic test complete" --voice af_heart --lang en-us --speed 1.0 \
  --output /tmp/kokoro-tts-diag-test.wav && afplay /tmp/kokoro-tts-diag-test.wav && echo "OK"

# Full health check
~/eon/cc-skills/plugins/tts-tg-sync/scripts/kokoro-install.sh --health

TodoWrite Task Templates

1. [Symptoms] Collect symptoms via AskUserQuestion
2. [Triage] Map symptoms to likely causes
3. [Lock] Check TTS lock state (mtime, PID, stale detection)
4. [Process] Check bot process and audio processes
5. [Kokoro] Verify Kokoro venv and MLX-Audio availability
6. [Logs] Check recent audit logs for errors
7. [Fix] Apply targeted fix for identified root cause
8. [Verify] Run health check to confirm resolution

Post-Change Checklist

  • Root cause identified and documented
  • Fix applied successfully
  • Health check passes
  • Test audio plays correctly
  • No stale locks or orphan processes remain

Troubleshooting

This skill IS the troubleshooting skill. If the standard diagnostics do not identify the issue:

  1. Check the full bot console log: cat /private/tmp/telegram-bot.log
  2. Check all NDJSON audit logs: ls -lt ~/.claude/automation/claude-telegram-sync/logs/audit/
  3. Check system audio: afplay /System/Library/Sounds/Tink.aiff (if this fails, it is a macOS audio issue, not TTS)
  4. Run a manual Kokoro generation outside the bot to isolate the problem
  5. If all else fails, do a full teardown and reinstall using clean-component-removal then full-stack-bootstrap

Reference Documentation

Post-Execution Reflection

After this skill completes, reflect before closing the task:

  1. Locate yourself. — Find this SKILL.md's canonical path (Glob for this skill's name) before editing. All corrections target THIS file and its sibling references/ — never other documentation.
  2. What failed? — Fix the instruction that caused it. If it could recur, add it as an anti-pattern.
  3. What worked better than expected? — Promote it to recommended practice. Document why.
  4. What drifted? — Any script, reference, or external dependency that no longer matches reality gets fixed now.
  5. Log it. — Every change gets an evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.



平台分布

Claude

33.01%
按下载量换算186

Codex

32.02%
按下载量换算180

Cursor

20.32%
按下载量换算114

Gemini CLI

9.49%
按下载量换算53

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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