
Claude Monitor
FreeDiagnose performance issues with Claude Code and local systems.
Free · Opens the source repo
What Claude Monitor does
Claude Monitor is a specialized tool designed to diagnose performance issues related to Claude Code and the local system. It helps users identify bottlenecks by measuring CPU, RAM, disk usage, and API latency, providing a comprehensive health report of the system. The skill is particularly useful for users experiencing slowdowns or lag, as it can pinpoint whether the issue is local (on the user's PC) or remote (related to the Claude API). With its built-in scripts, users can quickly assess their system's performance and receive actionable recommendations to resolve any identified issues.
The skill operates through a series of Python scripts that automate the diagnostic process. The first step involves running the health_check.py script, which analyzes key system metrics in approximately three seconds. This includes checking CPU usage, RAM availability, browser resource consumption, disk space, and network latency. Based on the results, the script provides a diagnosis in JSON format, detailing the nature of the bottleneck and offering suggestions for corrective actions. This allows users to take informed steps to improve their system's performance.
For ongoing monitoring, the skill includes an optional continuous monitoring feature that logs system performance over time. Users can set intervals and durations for monitoring, allowing them to track trends and receive alerts when performance thresholds are exceeded. Additionally, the skill includes an API benchmarking tool to assess the responsiveness of the Claude API, helping users determine if the lag is due to local issues or external factors.
Claude Monitor is ideal for developers and designers who rely on Claude Code for their work and need to ensure optimal performance. Its straightforward diagnostic approach and actionable insights make it a valuable addition to any workflow where performance issues may arise.
When to use it
Use this skill when experiencing slow performance with Claude Code or when system resources appear to be under strain.
When not to use it
Avoid using this skill for unrelated tasks or when a simpler, more specialized tool can achieve the desired outcome.
What you can build with it
Diagnosing Slow Claude Code
When a user reports that Claude Code is running slowly, run the health_check.py to identify the bottleneck.
Monitoring System Performance
Set up continuous monitoring during intensive tasks to track CPU and RAM usage over time.
Checking API Latency
Use the api_bench.py script to measure response times and determine if the API is contributing to performance issues.
How to install Claude Monitor
View source1. Install with the skills CLI
npx skills add sickn33/agentic-awesome-skills/claude-monitor --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by sickn33Claude Monitor — Diagnóstico de Performance
Overview
Monitor de performance do Claude Code e sistema local. Diagnostica lentidao, mede CPU/RAM/disco, verifica API latency e gera relatorios de saude do sistema.
When to Use This Skill
- When the user mentions "lento" or related topics
- When the user mentions "lentidao" or related topics
- When the user mentions "lag" or related topics
- When the user mentions "lagado" or related topics
- When the user mentions "travando" or related topics
- When the user mentions "claude lento" or related topics
Do Not Use This Skill When
- The task is unrelated to claude monitor
- A simpler, more specific tool can handle the request
- The user needs general-purpose assistance without domain expertise
How It Works
Skill para diagnosticar e resolver problemas de lentidão no Claude Code e no sistema. Determina se o gargalo é local (PC) ou remoto (API Claude) e sugere ações corretivas.
Quando Usar
- Usuário reclama que o Claude Code está lento ou travando
- Troca de sessões de conversa demora para carregar
- Respostas do Claude demoram muito
- PC parece lento enquanto usa o Claude Code
- Qualquer menção a performance, lag, lentidão
1. Diagnóstico Rápido (Health_Check.Py)
Rode SEMPRE como primeiro passo:
python C:\Users\renat\skills\claude-monitor\scripts\health_check.py
O script analisa em ~3 segundos:
- CPU: Uso atual e por core. >80% = gargalo provável
- RAM: Total, usada, disponível. >85% = pressão de memória
- Browsers: Processos e RAM por browser. >5GB total = excesso de abas
- Claude Code: Processos e RAM consumida
- Disco: Espaço livre. <10% = impacto em swap/performance
- Rede: Latência ao endpoint da API Claude
- Diagnóstico: Classificação automática do problema com sugestões
2. Interpretar O Resultado
O script retorna um JSON com diagnosis contendo:
bottleneck: "cpu" | "ram" | "browsers" | "disk" | "network" | "claude_api" | "ok"severity: "critical" | "warning" | "ok"suggestions: Lista de ações recomendadassummary: Resumo em português para mostrar ao usuário
Mostre o summary ao usuário e ofereça executar as sugestões.
3. Ações Corretivas Automáticas
Baseado no diagnóstico, ofereça ao usuário:
Se CPU alta (>80%):
- Listar processos consumindo mais CPU
- Sugerir fechar processos pesados desnecessários
- Verificar se Windows Update está rodando em background
Se browsers pesados (>5GB RAM ou >40 processos):
python C:\Users\renat\skills\claude-monitor\scripts\health_check.py --browsers-detail
Mostra RAM por browser e sugere quais fechar. Nunca fechar processos sem permissão explícita do usuário.
Se disco cheio (>85%):
- Mostrar pastas maiores
- Sugerir limpeza de Temp, cache de browsers, lixeira
Se rede lenta (latência >500ms):
- Testar conexão com api.anthropic.com
- Sugerir verificar VPN, proxy, ou conexão WiFi
4. Monitor Contínuo (Opcional)
Se o usuário quiser monitoramento em background:
python C:\Users\renat\skills\claude-monitor\scripts\monitor.py --interval 30 --duration 300
Parâmetros:
--interval: Segundos entre cada amostra (default: 30)--duration: Duração total em segundos (default: 300 = 5 min)--output: Caminho do arquivo de log (default: monitor_log.json)--alert-cpu: Threshold de CPU para alerta (default: 80)--alert-ram: Threshold de RAM % para alerta (default: 85)
O monitor salva snapshots periódicos e gera um relatório ao final com:
- Picos de CPU e RAM
- Tendência (melhorando/piorando/estável)
- Eventos de alerta detectados
- Recomendação final
5. Benchmark Da Api Claude (Opcional)
Para testar se a lentidão é da API:
python C:\Users\renat\skills\claude-monitor\scripts\api_bench.py
Mede o tempo de resposta do processo Claude Code local (não faz chamadas à API). Compara com tempos típicos e indica se está dentro do esperado.
Thresholds De Referência
| Métrica | OK | Warning | Critical |
|---|---|---|---|
| CPU % | <60% | 60-85% | >85% |
| RAM usada % | <70% | 70-85% | >85% |
| RAM browsers | <3 GB | 3-6 GB | >6 GB |
| Processos browser | <30 | 30-60 | >60 |
| Disco livre | >15% | 10-15% | <10% |
| Latência rede | <200ms | 200-500ms | >500ms |
Dicas Para O Usuário
Quando apresentar o diagnóstico, inclua estas dicas contextuais:
- Muitas abas = muito CPU/RAM: Cada aba de browser é um processo separado. 50 abas = 50 processos competindo por recursos.
- Claude Code é pesado: Ele roda vários processos Electron. É normal consumir 3-5 GB. Mas se estiver usando >6 GB com várias sessões, considere fechar sessões antigas.
- Troca de sessão lenta: Geralmente causada por CPU alta ou muitos processos competindo. A sessão precisa carregar o histórico da conversa, e se o CPU está ocupado, demora.
- Disco quase cheio: Afeta a velocidade do swap (memória virtual) e pode causar lentidão generalizada.
Dependências
- Python 3.10+
- psutil (instalado automaticamente pelo script se não disponível)
- Nenhuma API key necessária
Best Practices
- Provide clear, specific context about your project and requirements
- Review all suggestions before applying them to production code
- Combine with other complementary skills for comprehensive analysis
Common Pitfalls
- Using this skill for tasks outside its domain expertise
- Applying recommendations without understanding your specific context
- Not providing enough project context for accurate analysis
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Frequently asked questions about Claude Monitor
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