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monitormonitor 搜索

Agent Skill

monitor 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

857

周安装

35

GitHub Stars

167

下载量

277
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:monitor(monitor 搜索)
来源仓库:https://github.com/whawkinsiv/claude-code-superpowers
仓库路径:skills/monitor
安装命令:
npx skills add https://github.com/whawkinsiv/claude-code-superpowers --skill monitor
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/whawkinsiv/claude-code-superpowers --skill monitor

简介

monitor 用于查找、检索和筛选相关信息。

  • 适用于监控类任务中的信息支持与数据分析。
  • 支持在主流 AI 宿主中安装使用。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。
  • 使用前应确认其是否具备访问监控系统或日志文件的权限。
  • monitor 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Monitor

This skill is for production monitoring and incident response. For debugging specific bugs, use debug. For pre-launch readiness checks, use go-live. For security-specific monitoring (auth events, API abuse), use secure. For analytics and user behavior tracking, use analytics.

Don't Do Yet

  • Don't pay for monitoring tools until you've outgrown the free tiers. UptimeRobot + Sentry free handles most early-stage apps.
  • Don't set up DataDog, New Relic, or Grafana. These are enterprise tools. You don't need them with < 1,000 users.
  • Don't build custom dashboards. Your hosting platform (Vercel, Railway) has built-in metrics. Use those first.
  • Don't monitor everything. Three things matter at launch: is it up, are there errors, is it slow. That's it.

Monitoring Checklist

Basic Monitoring:
- [ ] Uptime monitoring (is site up?)
- [ ] Error tracking (are errors happening?)
- [ ] Performance monitoring (is it slow?)
- [ ] User activity (are people using it?)
- [ ] Critical alerts configured
- [ ] Check dashboard daily

See MONITORING-SETUP.md for implementation.


Why Monitor?

Without monitoring:

  • Users hit errors, you don't know
  • Site goes down, you find out from Twitter
  • Slow performance, users leave silently
  • Security issues, no alert

With monitoring:

  • Errors show in dashboard immediately
  • Get text when site goes down
  • See performance degradation
  • Catch issues before users complain

Goal: Know about problems before users tell you.


Three Essential Monitors

1. Is It Up?

Uptime monitoring - Pings your app every minute

Free tools:

  • UptimeRobot (free, 50 monitors)
  • Pingdom (limited free tier)
  • Vercel/Netlify (built-in for deployed apps)

Setup:

1. Sign up for UptimeRobot
2. Add monitor for https://yourapp.com
3. Add your email for alerts
4. Get texted if site is down

2. Are There Errors?

Error tracking - Captures JavaScript errors and API failures

Free tools:

  • Sentry (free tier: 5k errors/month)
  • LogRocket (limited free)
  • Vercel/Netlify logs (for deployed apps)

Claude Code:

Add Sentry error tracking to my app:
- Install @sentry/nextjs (or appropriate package)
- Capture all frontend errors and API errors
- Include user context (email, ID)
- Configure source maps for readable stack traces
- Set up Sentry.init in both client and server entry points

Lovable / Replit (paste into chat):

Add error tracking to my app. I want to be notified when errors happen.
Use Sentry (free tier). Show me how to:
1. Create a Sentry account and project
2. Add the tracking code to my app
3. Test that errors are being captured

3. Is It Slow?

Performance monitoring - Tracks page load times

Free tools:

  • Vercel Analytics (built-in)
  • Google PageSpeed Insights (free)
  • Cloudflare Analytics (free tier)

Setup:

  • Usually automatic with hosting platform
  • Check dashboard weekly

What to Monitor

Critical Metrics

Must monitor:

  • Site uptime (99%+)
  • Error rate (< 1% of requests)
  • API response time (< 500ms)
  • Page load time (< 3s)

Nice to have:

  • Active users
  • Feature usage
  • Conversion rates
  • User paths

For MVP: Focus on the "must monitor" only.


Setting Up Alerts

Configure alerts for:

Critical (text me immediately):

  • Site is down
  • Error rate spike (10x normal)
  • Database connection lost
  • Payment processing failing

Important (email within hour):

  • API slow (>2 seconds)
  • Error rate elevated (2x normal)
  • Disk space low (>80%)

Informational (daily digest):

  • New errors discovered
  • Performance trending down
  • Traffic patterns

Tell AI:

Configure monitoring alerts:
- Critical: Text to [phone]
- Important: Email to [email]
- Send summary: Daily at 9am

Daily Monitoring Routine

5-minute morning check:

Daily Check:
1. Open monitoring dashboard
2. Check uptime (should be 100% yesterday)
3. Check error count (any spikes?)
4. Check performance (slower than usual?)
5. Review any alerts from overnight

If all green: You're done, 5 minutes.

If red: Investigate using debug skill.


Reading Monitoring Dashboards

Uptime Dashboard

Green: Site responding Red: Site down or slow to respond

What to check:

  • Uptime percentage (target: 99%+)
  • Response time (target: <500ms)
  • Recent downtime incidents

Error Dashboard

Look for:

  • Error count spikes (sudden jump)
  • New error types (didn't see before)
  • Affected users (how many hit this?)
  • Error frequency (happening a lot?)

Priority:

  • Affecting many users → High priority
  • Blocking key features → High priority
  • Edge case error → Lower priority

Performance Dashboard

Look for:

  • Load time trending up (getting slower)
  • Slow endpoints (which API calls)
  • Slow pages (which routes)
  • Geographic differences (slow in specific regions)

Error Investigation

When errors spike:

1. Open error tracking dashboard (Sentry)
2. Find the most frequent error
3. Read error message and stack trace
4. Note: How many users affected?
5. Note: Started when?
6. Check: Did we deploy recently?

Give to AI:

Error in production:
[Paste error message and stack trace]

Affected: [X] users in last [Y] hours
Started: [timestamp]
Recent deploys: [any?]

Please:
1. Explain what's wrong
2. Propose hotfix
3. How to test before deploying

User-Reported Issues

When user reports problem:

User Report Investigation:
1. Can you reproduce it?
2. Check monitoring for errors at that time
3. Check logs for that user
4. Check if others affected
5. Determine severity

Then use debug skill to fix.

Tell AI:

User reported: [issue description]
User: [email or ID]
Timestamp: [when it happened]

Check monitoring and logs for this user at this time.
What errors or issues do you see?

Proactive Monitoring

Catch issues before users:

Weekly checks:

Weekly Review:
- [ ] Error trends (going up or down?)
- [ ] Performance trends (slower?)
- [ ] New error types introduced
- [ ] Uptime issues resolved
- [ ] Alert noise (too many false alerts?)

Monthly checks:

Monthly Health:
- [ ] Compare to last month
- [ ] Any degradation?
- [ ] Any improvements?
- [ ] Monitoring gaps (what's not tracked?)

Free Monitoring Stack

Recommended for MVP:

Uptime:

  • UptimeRobot (free) - 50 monitors

Errors:

  • Sentry (free) - 5k errors/month

Performance:

  • Vercel Analytics (free on Vercel)
  • Cloudflare Analytics (free)

Logs:

  • Platform logs (Vercel, Netlify, Railway)

Cost: $0/month until you need more.


When to Upgrade Monitoring

Upgrade when:

  • Hitting free tier limits
  • Need more detailed analytics
  • Need faster alert response
  • Need advanced features (session replay, etc.)

Paid tiers (typically $20-50/mo):

  • Sentry Pro ($26/mo)
  • LogRocket ($99/mo - session replay)
  • DataDog ($15/host/mo)

For < 1000 users: Free tiers sufficient.


Common Monitoring Mistakes

MistakeFix
No monitoring set upSet up before launch
Alert fatigue (too many alerts)Only alert on critical issues
Checking once a monthCheck daily (5 minutes)
Ignoring trendsWatch for degradation over time
No alerts configuredSet up text alerts for downtime
Monitoring but not actingUse monitoring to find and fix issues

Interpreting Trends

Good trends:

  • Errors decreasing
  • Performance improving
  • Uptime stable at 99.9%+

Warning trends:

  • Errors slowly increasing
  • Performance slowly degrading
  • Uptime dipping below 99%

Critical trends:

  • Sudden error spike
  • Sudden performance drop
  • Multiple downtime incidents

Action: Address warning trends before they become critical.


Logging vs Monitoring

Logging:

  • Records what happened
  • For debugging specific issues
  • Detailed, verbose
  • Review when investigating

Monitoring:

  • Tracks overall health
  • For catching issues early
  • High-level metrics
  • Review daily

Both needed: Monitoring alerts you, logs help debug.


Setting Up Logging

Tell AI:

Add application logging:
- Log all errors with context
- Log API requests/responses
- Log slow operations (>1s)
- Log authentication events
- Don't log sensitive data

Format: JSON with timestamp, level, message, context
Send to: [Platform logs or external service]

Log levels:

  • ERROR: Something broke
  • WARN: Something concerning
  • INFO: Normal operations
  • DEBUG: Detailed debugging info

Production: Log ERROR and WARN only.


Monitoring Integrations

Third-party services:

Payments (Stripe):

  • Failed payments alert
  • Refund requests alert
  • Subscription cancellations (daily digest)

Email (SendGrid):

  • Delivery failures alert
  • Bounce rate elevated alert
  • Spam complaints alert

Database:

  • Connection pool exhausted
  • Slow queries (>1s)
  • Disk space low

Tell AI:

Add monitoring for [service]:
- Alert on failures
- Track success rate
- Log errors with context

Incident Response

When alerts fire:

Incident Response:
1. Acknowledge alert (mark as seen)
2. Assess severity:
   - Critical: Site down, payments failing
   - High: Errors affecting many users
   - Medium: Isolated issues
3. Immediate action:
   - Critical: Hotfix or rollback
   - High: Fix within hours
   - Medium: Fix in next deploy
4. Update users if needed
5. Post-mortem after resolved

Critical incidents:

1. Assess impact (how many affected?)
2. Quick fix or rollback
3. Deploy hotfix
4. Verify fixed
5. Monitor closely for hour
6. Update status page if you have one

Success Looks Like

✅ Know about issues before users report them ✅ Uptime >99.9% ✅ Errors caught and fixed quickly ✅ Performance trends stable or improving ✅ Daily monitoring routine (5 minutes) ✅ Alerts configured and actionable ✅ Issues resolved proactively


Related Skills

  • debug — Investigate and fix specific bugs
  • deploy — Hosting setup and rollback procedures
  • secure — Security monitoring and hardening
  • analytics — User behavior tracking and conversion funnels
  • go-live — Pre-launch readiness (includes monitoring as a checklist item)

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

34.69%
按下载量换算96

Claude

33.7%
按下载量换算93

Cursor

19.08%
按下载量换算53

Gemini CLI

9.57%
按下载量换算27

安全审计

Gen Agent Trust Hub

通过

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 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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