📦 deps(thirdparty): update snapshots

2026-06-21 09:15:23 +00:00
parent 0e1bb1aef3
commit 3d137606c0
805 changed files with 93512 additions and 4532 deletions
@@ -1,6 +1,6 @@
 {
  "name": "antigravity-bundle-aas-observability-ir",
-  "version": "12.9.0",
+  "version": "13.0.0",
  "description": "Editorial \"AAS Observability IR\" bundle for Claude Code from Antigravity Awesome Skills.",
  "author": {
    "name": "sickn33 and contributors",
@@ -1,7 +1,7 @@
 {
  "name": "agyb-aas-observability-ir",
-  "version": "12.9.0",
-  "description": "Install the \"AAS Observability IR\" editorial skill bundle from Antigravity Awesome Skills.",
+  "version": "13.0.0",
+  "description": "Install the \"AAS Observability IR\" workflow plugin from Antigravity Awesome Skills.",
  "author": {
    "name": "sickn33 and contributors",
    "url": "https://github.com/sickn33/antigravity-awesome-skills"
@@ -19,8 +19,8 @@
  "skills": "./skills/",
  "interface": {
    "displayName": "AAS Observability IR",
-    "shortDescription": "Specialized Product Plugins - Next Wave · 8 curated skills",
-    "longDescription": "Engineering teams monitoring systems, debugging production issues, and writing postmortems. Covers Observability Engineer, Distributed Tracing, and 6 more skills.",
+    "shortDescription": "Design observability, SLOs, traces, dashboards, monitoring, incident response, troubleshooting, and postmortem workflows.",
+    "longDescription": "Design observability, SLOs, traces, dashboards, monitoring, incident response, troubleshooting, and postmortem workflows. Operational work needs consistent procedure and proof gates, making it more plugin-worthy than isolated observability prompts. Recommended for: SRE teams, Backend teams owning production, Incident responders. Not for: Marketing campaign planning, Static document conversion. Covers Observability Engineer, Distributed Tracing, and 8 more skills.",
    "developerName": "sickn33 and contributors",
    "category": "Specialized Product Plugins - Next Wave",
    "capabilities": [
@@ -28,6 +28,11 @@
      "Write"
    ],
    "websiteURL": "https://github.com/sickn33/antigravity-awesome-skills",
-    "brandColor": "#111827"
+    "brandColor": "#111827",
+    "defaultPrompt": [
+      "Use this plugin to review this service for logging, tracing, SLO, monitoring, and dashboard gaps.",
+      "Use this plugin to build an incident response plan and rollback checklist for this system.",
+      "Use this plugin to draft a blameless postmortem from this timeline and identify action items."
+    ]
  }
 }
@@ -0,0 +1,183 @@
+---
+name: claude-monitor
+description: 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.
+risk: safe
+source: community
+date_added: '2026-03-06'
+author: renat
+tags:
+- monitoring
+- performance
+- diagnostics
+- system-health
+tools:
+- claude-code
+- antigravity
+- cursor
+- gemini-cli
+- codex-cli
+---
+
+# Claude 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:
+
+```bash
+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 recomendadas
+- `summary`: 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):
+```bash
+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:
+
+```bash
+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:
+
+```bash
+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.
@@ -0,0 +1,251 @@
+#!/usr/bin/env python3
+"""
+Claude Monitor — Benchmark de Conectividade API
+
+Testa latência e conectividade com a API do Claude.
+Não faz chamadas à API (não precisa de API key).
+Apenas verifica se a rede está funcionando e se o endpoint responde.
+
+Uso:
+    python api_bench.py              # 5 testes de latência
+    python api_bench.py --samples 10 # 10 testes
+    python api_bench.py --json       # Output JSON
+"""
+
+import json
+import socket
+import ssl
+import subprocess
+import sys
+import time
+from datetime import datetime
+
+try:
+    import psutil
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "psutil", "--quiet"])
+    import psutil
+
+
+ENDPOINTS = [
+    {"name": "Claude API", "host": "api.anthropic.com", "port": 443},
+    {"name": "Anthropic CDN", "host": "cdn.anthropic.com", "port": 443},
+    {"name": "Google DNS", "host": "8.8.8.8", "port": 53},
+]
+
+
+def create_tls_context():
+    """Cria contexto TLS restringindo conexoes a TLS 1.2+."""
+    context = ssl.create_default_context()
+    if hasattr(ssl, "TLSVersion"):
+        context.minimum_version = ssl.TLSVersion.TLSv1_2
+    else:
+        context.options |= getattr(ssl, "OP_NO_TLSv1", 0)
+        context.options |= getattr(ssl, "OP_NO_TLSv1_1", 0)
+    return context
+
+
+def test_tcp_latency(host, port, timeout=5):
+    """Testa latência TCP para um host:port."""
+    try:
+        start = time.time()
+        sock = socket.create_connection((host, port), timeout=timeout)
+        latency = (time.time() - start) * 1000  # ms
+        sock.close()
+        return {"reachable": True, "latency_ms": round(latency, 1)}
+    except (socket.timeout, socket.error, OSError) as e:
+        return {"reachable": False, "latency_ms": None, "error": str(e)}
+
+
+def test_tls_handshake(host, port=443, timeout=5):
+    """Testa tempo do handshake TLS."""
+    try:
+        context = create_tls_context()
+        start = time.time()
+        with socket.create_connection((host, port), timeout=timeout) as sock:
+            with context.wrap_socket(sock, server_hostname=host) as ssock:
+                handshake_time = (time.time() - start) * 1000
+                return {
+                    "success": True,
+                    "handshake_ms": round(handshake_time, 1),
+                    "tls_version": ssock.version(),
+                }
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+def test_dns(hostname):
+    """Testa resolução DNS."""
+    try:
+        start = time.time()
+        ip = socket.gethostbyname(hostname)
+        dns_time = (time.time() - start) * 1000
+        return {"resolved": True, "ip": ip, "dns_ms": round(dns_time, 1)}
+    except socket.gaierror as e:
+        return {"resolved": False, "error": str(e)}
+
+
+def check_network_interfaces():
+    """Verifica interfaces de rede ativas."""
+    stats = psutil.net_if_stats()
+    active = []
+    for name, info in stats.items():
+        if info.isup and info.speed > 0:
+            active.append({
+                "name": name,
+                "speed_mbps": info.speed,
+                "mtu": info.mtu,
+            })
+    return active
+
+
+def run_benchmark(samples=5):
+    """Roda o benchmark completo."""
+    results = {
+        "timestamp": datetime.now().isoformat(),
+        "samples": samples,
+        "endpoints": [],
+        "dns": None,
+        "tls": None,
+        "network_interfaces": check_network_interfaces(),
+    }
+
+    # DNS
+    results["dns"] = test_dns("api.anthropic.com")
+
+    # TLS handshake
+    results["tls"] = test_tls_handshake("api.anthropic.com")
+
+    # Latência por endpoint
+    for ep in ENDPOINTS:
+        latencies = []
+        for _ in range(samples):
+            result = test_tcp_latency(ep["host"], ep["port"])
+            latencies.append(result)
+            time.sleep(0.2)
+
+        valid = [r["latency_ms"] for r in latencies if r["reachable"] and r["latency_ms"]]
+
+        ep_result = {
+            "name": ep["name"],
+            "host": ep["host"],
+            "port": ep["port"],
+            "tests": latencies,
+        }
+
+        if valid:
+            ep_result["avg_ms"] = round(sum(valid) / len(valid), 1)
+            ep_result["min_ms"] = round(min(valid), 1)
+            ep_result["max_ms"] = round(max(valid), 1)
+            ep_result["success_rate"] = round(len(valid) / samples * 100, 0)
+        else:
+            ep_result["avg_ms"] = None
+            ep_result["success_rate"] = 0
+
+        results["endpoints"].append(ep_result)
+
+    # Diagnóstico
+    api_ep = results["endpoints"][0]
+    if api_ep.get("avg_ms") is None:
+        results["diagnosis"] = {
+            "status": "critical",
+            "message": "API do Claude INACESSIVEL. Verifique sua conexao de internet.",
+        }
+    elif api_ep["avg_ms"] > 500:
+        results["diagnosis"] = {
+            "status": "warning",
+            "message": (
+                f"Latencia alta para API ({api_ep['avg_ms']}ms). "
+                f"Conexao lenta pode causar atrasos no Claude Code."
+            ),
+        }
+    elif api_ep["avg_ms"] > 200:
+        results["diagnosis"] = {
+            "status": "ok",
+            "message": (
+                f"Latencia moderada ({api_ep['avg_ms']}ms). "
+                f"Dentro do aceitavel mas pode ser melhor."
+            ),
+        }
+    else:
+        results["diagnosis"] = {
+            "status": "ok",
+            "message": (
+                f"Conexao excelente ({api_ep['avg_ms']}ms). "
+                f"A rede NAO e o gargalo."
+            ),
+        }
+
+    return results
+
+
+def format_results(results):
+    """Formata resultados para exibição."""
+    lines = ["## Benchmark de Conectividade\n"]
+
+    # DNS
+    dns = results["dns"]
+    if dns.get("resolved"):
+        lines.append(f"- DNS: api.anthropic.com -> {dns['ip']} ({dns['dns_ms']}ms)")
+    else:
+        lines.append(f"- DNS: FALHOU ({dns.get('error', 'desconhecido')})")
+
+    # TLS
+    tls = results["tls"]
+    if tls.get("success"):
+        lines.append(f"- TLS: {tls['tls_version']} handshake em {tls['handshake_ms']}ms")
+    else:
+        lines.append(f"- TLS: FALHOU ({tls.get('error', 'desconhecido')})")
+
+    lines.append("")
+
+    # Endpoints
+    lines.append("### Latencia por Endpoint")
+    for ep in results["endpoints"]:
+        if ep.get("avg_ms"):
+            lines.append(
+                f"- **{ep['name']}**: {ep['avg_ms']}ms avg "
+                f"(min {ep['min_ms']}ms, max {ep['max_ms']}ms) "
+                f"[{ep['success_rate']:.0f}% sucesso]"
+            )
+        else:
+            lines.append(f"- **{ep['name']}**: INACESSIVEL")
+
+    # Interfaces
+    lines.append("\n### Interfaces de Rede")
+    for iface in results["network_interfaces"]:
+        speed = iface["speed_mbps"]
+        if speed >= 1000:
+            speed_str = f"{speed/1000:.0f} Gbps"
+        else:
+            speed_str = f"{speed} Mbps"
+        lines.append(f"- {iface['name']}: {speed_str}")
+
+    # Diagnóstico
+    lines.append(f"\n### Diagnostico")
+    diag = results["diagnosis"]
+    status_map = {"critical": "[!!!]", "warning": "[!]", "ok": "[OK]"}
+    lines.append(f"{status_map[diag['status']]} {diag['message']}")
+
+    return "\n".join(lines)
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Claude Monitor - Benchmark de Conectividade")
+    parser.add_argument("--samples", type=int, default=5, help="Numero de testes por endpoint")
+    parser.add_argument("--json", action="store_true", help="Output JSON")
+    args = parser.parse_args()
+
+    print(f"Testando conectividade ({args.samples} amostras por endpoint)...\n")
+    results = run_benchmark(args.samples)
+
+    if args.json:
+        print(json.dumps(results, indent=2, ensure_ascii=False))
+    else:
+        print(format_results(results))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,69 @@
+"""
+Configurações e thresholds para o Claude Monitor.
+"""
+
+# Thresholds de alerta
+THRESHOLDS = {
+    "cpu": {
+        "ok": 60,
+        "warning": 85,
+        # acima de warning = critical
+    },
+    "ram_percent": {
+        "ok": 70,
+        "warning": 85,
+    },
+    "browsers_ram_gb": {
+        "ok": 3.0,
+        "warning": 6.0,
+    },
+    "browsers_processes": {
+        "ok": 30,
+        "warning": 60,
+    },
+    "disk_free_percent": {
+        "critical_below": 10,
+        "warning_below": 15,
+    },
+    "network_latency_ms": {
+        "ok": 200,
+        "warning": 500,
+    },
+}
+
+# Nomes de processos de browser conhecidos
+BROWSER_NAMES = ["chrome", "msedge", "firefox", "brave", "opera", "vivaldi"]
+
+# Nomes de processos do Claude Code
+CLAUDE_NAMES = ["claude"]
+
+# Endpoint para teste de latência
+API_ENDPOINT = "api.anthropic.com"
+
+# Monitor defaults
+MONITOR_DEFAULTS = {
+    "interval": 30,
+    "duration": 300,
+    "alert_cpu": 80,
+    "alert_ram": 85,
+}
+
+
+def classify(value, metric_name):
+    """Classifica um valor como 'ok', 'warning' ou 'critical'."""
+    t = THRESHOLDS.get(metric_name, {})
+
+    # Métricas onde "abaixo" é ruim (disco livre)
+    if "critical_below" in t:
+        if value < t["critical_below"]:
+            return "critical"
+        elif value < t["warning_below"]:
+            return "warning"
+        return "ok"
+
+    # Métricas onde "acima" é ruim (CPU, RAM, latência)
+    if value <= t.get("ok", 999999):
+        return "ok"
+    elif value <= t.get("warning", 999999):
+        return "warning"
+    return "critical"
@@ -0,0 +1,362 @@
+#!/usr/bin/env python3
+"""
+Claude Monitor — Diagnóstico Rápido de Performance
+
+Analisa CPU, RAM, browsers, disco e rede em ~3 segundos.
+Identifica o gargalo principal e sugere ações corretivas.
+
+Uso:
+    python health_check.py                  # Diagnóstico completo
+    python health_check.py --browsers-detail  # Detalhe de browsers
+    python health_check.py --json            # Output JSON puro
+    python health_check.py --quick           # Só resumo (sem teste de rede)
+"""
+
+import json
+import os
+import socket
+import subprocess
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+# Garante que psutil está disponível
+try:
+    import psutil
+except ImportError:
+    print("Instalando psutil...")
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "psutil", "--quiet"])
+    import psutil
+
+# Importa config do mesmo diretório
+sys.path.insert(0, str(Path(__file__).parent))
+from config import (
+    BROWSER_NAMES, CLAUDE_NAMES, API_ENDPOINT,
+    THRESHOLDS, classify
+)
+
+
+def check_cpu():
+    """Verifica uso de CPU."""
+    cpu_percent = psutil.cpu_percent(interval=1)
+    cpu_count = psutil.cpu_count()
+    per_cpu = psutil.cpu_percent(interval=0, percpu=True)
+
+    return {
+        "percent": cpu_percent,
+        "cores": cpu_count,
+        "per_core": per_cpu,
+        "status": classify(cpu_percent, "cpu"),
+    }
+
+
+def check_ram():
+    """Verifica uso de RAM."""
+    ram = psutil.virtual_memory()
+    swap = psutil.swap_memory()
+
+    return {
+        "total_gb": round(ram.total / 1024**3, 1),
+        "used_gb": round(ram.used / 1024**3, 1),
+        "available_gb": round(ram.available / 1024**3, 1),
+        "percent": ram.percent,
+        "swap_used_gb": round(swap.used / 1024**3, 1),
+        "swap_percent": swap.percent,
+        "status": classify(ram.percent, "ram_percent"),
+    }
+
+
+def check_browsers(detail=False):
+    """Verifica processos de browser e consumo de RAM."""
+    browsers = {}
+    all_procs = []
+
+    for proc in psutil.process_iter(["pid", "name", "memory_info"]):
+        try:
+            info = proc.info
+            name_lower = info["name"].lower()
+            ram_mb = info["memory_info"].rss / 1024**2
+
+            for bname in BROWSER_NAMES:
+                if bname in name_lower:
+                    if bname not in browsers:
+                        browsers[bname] = {"count": 0, "ram_mb": 0, "pids": []}
+                    browsers[bname]["count"] += 1
+                    browsers[bname]["ram_mb"] += ram_mb
+                    if detail:
+                        browsers[bname]["pids"].append({
+                            "pid": info["pid"],
+                            "ram_mb": round(ram_mb, 0)
+                        })
+                    break
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            pass
+
+    total_ram_gb = sum(b["ram_mb"] for b in browsers.values()) / 1024
+    total_procs = sum(b["count"] for b in browsers.values())
+
+    # Formata para output
+    for bname in browsers:
+        browsers[bname]["ram_mb"] = round(browsers[bname]["ram_mb"], 0)
+
+    return {
+        "browsers": browsers,
+        "total_ram_gb": round(total_ram_gb, 1),
+        "total_processes": total_procs,
+        "ram_status": classify(total_ram_gb, "browsers_ram_gb"),
+        "process_status": classify(total_procs, "browsers_processes"),
+    }
+
+
+def check_claude_processes():
+    """Verifica processos do Claude Code."""
+    claude_procs = []
+    total_ram = 0
+
+    for proc in psutil.process_iter(["pid", "name", "memory_info", "cpu_percent"]):
+        try:
+            info = proc.info
+            name_lower = info["name"].lower()
+
+            for cname in CLAUDE_NAMES:
+                if cname in name_lower:
+                    ram_mb = info["memory_info"].rss / 1024**2
+                    claude_procs.append({
+                        "pid": info["pid"],
+                        "name": info["name"],
+                        "ram_mb": round(ram_mb, 0),
+                    })
+                    total_ram += ram_mb
+                    break
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            pass
+
+    claude_procs.sort(key=lambda x: x["ram_mb"], reverse=True)
+
+    return {
+        "count": len(claude_procs),
+        "total_ram_gb": round(total_ram / 1024, 1),
+        "processes": claude_procs[:10],  # Top 10
+    }
+
+
+def check_disk():
+    """Verifica espaço em disco."""
+    disk = psutil.disk_usage("C:/")
+    free_percent = 100 - disk.percent
+
+    return {
+        "total_gb": round(disk.total / 1024**3, 0),
+        "used_gb": round(disk.used / 1024**3, 0),
+        "free_gb": round(disk.free / 1024**3, 0),
+        "used_percent": disk.percent,
+        "free_percent": round(free_percent, 1),
+        "status": classify(free_percent, "disk_free_percent"),
+    }
+
+
+def check_network():
+    """Testa latência até a API do Claude."""
+    try:
+        start = time.time()
+        sock = socket.create_connection((API_ENDPOINT, 443), timeout=5)
+        latency_ms = round((time.time() - start) * 1000, 0)
+        sock.close()
+
+        return {
+            "latency_ms": latency_ms,
+            "endpoint": API_ENDPOINT,
+            "reachable": True,
+            "status": classify(latency_ms, "network_latency_ms"),
+        }
+    except (socket.timeout, socket.error, OSError) as e:
+        return {
+            "latency_ms": None,
+            "endpoint": API_ENDPOINT,
+            "reachable": False,
+            "status": "critical",
+            "error": str(e),
+        }
+
+
+def check_top_processes(n=10):
+    """Lista os N processos que mais consomem RAM."""
+    procs = []
+    for proc in psutil.process_iter(["pid", "name", "memory_info"]):
+        try:
+            info = proc.info
+            procs.append({
+                "name": info["name"],
+                "ram_mb": round(info["memory_info"].rss / 1024**2, 0),
+                "pid": info["pid"],
+            })
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            pass
+
+    procs.sort(key=lambda x: x["ram_mb"], reverse=True)
+    return procs[:n]
+
+
+def diagnose(results):
+    """Analisa os resultados e gera diagnóstico."""
+    issues = []
+    suggestions = []
+    bottleneck = "ok"
+    severity = "ok"
+
+    cpu = results["cpu"]
+    ram = results["ram"]
+    browsers = results["browsers"]
+    disk = results["disk"]
+    network = results.get("network", {})
+    claude = results["claude"]
+
+    # CPU
+    if cpu["status"] == "critical":
+        issues.append(f"CPU a {cpu['percent']}% (CRITICO)")
+        suggestions.append("Fechar aplicativos pesados ou abas de browser desnecessarias")
+        suggestions.append("Verificar se Windows Update ou antivirus esta rodando em background")
+        bottleneck = "cpu"
+        severity = "critical"
+    elif cpu["status"] == "warning":
+        issues.append(f"CPU a {cpu['percent']}% (elevada)")
+        suggestions.append("Considerar fechar algumas abas de browser")
+        if severity != "critical":
+            bottleneck = "cpu"
+            severity = "warning"
+
+    # RAM
+    if ram["status"] == "critical":
+        issues.append(f"RAM a {ram['percent']}% ({ram['used_gb']} de {ram['total_gb']} GB)")
+        suggestions.append("Fechar browsers ou aplicativos para liberar memoria")
+        if severity != "critical":
+            bottleneck = "ram"
+            severity = "critical"
+    elif ram["status"] == "warning":
+        issues.append(f"RAM a {ram['percent']}% (monitorar)")
+
+    # Browsers
+    if browsers["ram_status"] == "critical":
+        issues.append(f"Browsers consumindo {browsers['total_ram_gb']} GB ({browsers['total_processes']} processos)")
+        suggestions.append("Fechar abas desnecessarias nos browsers")
+        browser_detail = []
+        for bname, info in browsers["browsers"].items():
+            browser_detail.append(f"  - {bname}: {info['count']} processos, {info['ram_mb']:.0f} MB")
+        suggestions.append("Detalhamento:\n" + "\n".join(browser_detail))
+        if bottleneck == "ok":
+            bottleneck = "browsers"
+            if severity == "ok":
+                severity = "warning"
+    elif browsers["ram_status"] == "warning":
+        issues.append(f"Browsers usando {browsers['total_ram_gb']} GB (moderado)")
+
+    # Disco
+    if disk["status"] == "critical":
+        issues.append(f"Disco quase cheio: apenas {disk['free_gb']:.0f} GB livres ({disk['free_percent']}%)")
+        suggestions.append("Limpar arquivos temporarios, cache e lixeira")
+        suggestions.append("Verificar pasta Downloads e Temp por arquivos grandes")
+        if bottleneck == "ok":
+            bottleneck = "disk"
+            severity = "warning"
+    elif disk["status"] == "warning":
+        issues.append(f"Disco com {disk['free_gb']:.0f} GB livres ({disk['free_percent']}%)")
+
+    # Rede
+    if network.get("status") == "critical":
+        if not network.get("reachable"):
+            issues.append("API do Claude INACESSIVEL")
+            suggestions.append("Verificar conexao com internet")
+            suggestions.append("Verificar se VPN ou proxy esta bloqueando")
+            bottleneck = "network"
+            severity = "critical"
+        else:
+            issues.append(f"Latencia alta para API: {network['latency_ms']}ms")
+            suggestions.append("Verificar qualidade da conexao WiFi/cabo")
+            if bottleneck == "ok":
+                bottleneck = "network"
+                severity = "warning"
+
+    # Claude Code RAM
+    if claude["total_ram_gb"] > 8:
+        issues.append(f"Claude Code usando {claude['total_ram_gb']} GB ({claude['count']} processos)")
+        suggestions.append("Considerar fechar sessoes de conversa antigas no Claude Code")
+
+    # Tudo ok
+    if not issues:
+        issues.append("Sistema saudavel, sem gargalos detectados")
+        suggestions.append("A lentidao pode ser temporaria (pico na API do Claude)")
+        suggestions.append("Tente trocar de sessao novamente em alguns segundos")
+
+    # Gerar resumo em PT-BR
+    summary_lines = ["## Diagnostico de Performance\n"]
+
+    status_emoji = {"critical": "[!!!]", "warning": "[!]", "ok": "[OK]"}
+    summary_lines.append(f"**Status geral: {status_emoji[severity]} {severity.upper()}**\n")
+
+    if bottleneck != "ok":
+        summary_lines.append(f"**Gargalo principal: {bottleneck.upper()}**\n")
+
+    summary_lines.append("### Problemas detectados:")
+    for issue in issues:
+        summary_lines.append(f"- {issue}")
+
+    summary_lines.append("\n### Acoes recomendadas:")
+    for i, sug in enumerate(suggestions, 1):
+        if "\n" in sug:
+            summary_lines.append(f"{i}. {sug}")
+        else:
+            summary_lines.append(f"{i}. {sug}")
+
+    summary_lines.append(f"\n### Numeros-chave:")
+    summary_lines.append(f"- CPU: {cpu['percent']}% | RAM: {ram['percent']}% ({ram['used_gb']}/{ram['total_gb']} GB)")
+    summary_lines.append(f"- Browsers: {browsers['total_processes']} processos, {browsers['total_ram_gb']} GB")
+    summary_lines.append(f"- Claude Code: {claude['count']} processos, {claude['total_ram_gb']} GB")
+    summary_lines.append(f"- Disco C: {disk['free_gb']:.0f} GB livres ({disk['free_percent']}%)")
+    if network.get("latency_ms"):
+        summary_lines.append(f"- Latencia API: {network['latency_ms']}ms")
+
+    return {
+        "bottleneck": bottleneck,
+        "severity": severity,
+        "issues": issues,
+        "suggestions": suggestions,
+        "summary": "\n".join(summary_lines),
+    }
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Claude Monitor - Diagnostico Rapido")
+    parser.add_argument("--browsers-detail", action="store_true", help="Mostra detalhes por browser")
+    parser.add_argument("--json", action="store_true", help="Output em JSON puro")
+    parser.add_argument("--quick", action="store_true", help="Pula teste de rede")
+    args = parser.parse_args()
+
+    results = {}
+
+    # Coleta dados
+    results["timestamp"] = datetime.now().isoformat()
+    results["cpu"] = check_cpu()
+    results["ram"] = check_ram()
+    results["browsers"] = check_browsers(detail=args.browsers_detail)
+    results["claude"] = check_claude_processes()
+    results["disk"] = check_disk()
+    results["top_processes"] = check_top_processes(15)
+
+    if not args.quick:
+        results["network"] = check_network()
+
+    # Diagnóstico
+    results["diagnosis"] = diagnose(results)
+
+    if args.json:
+        print(json.dumps(results, indent=2, ensure_ascii=False))
+    else:
+        print(results["diagnosis"]["summary"])
+        print(f"\n(Para output completo em JSON, use: python health_check.py --json)")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,296 @@
+#!/usr/bin/env python3
+"""
+Claude Monitor — Monitor Contínuo de Performance
+
+Coleta snapshots periódicos de CPU, RAM e browsers.
+Gera relatório com tendências e alertas ao final.
+
+Uso:
+    python monitor.py                          # 5 min, amostras a cada 30s
+    python monitor.py --interval 10 --duration 120  # 2 min, amostras a cada 10s
+    python monitor.py --output meu_log.json    # Salvar em arquivo específico
+"""
+
+import json
+import os
+import signal
+import subprocess
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+try:
+    import psutil
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "psutil", "--quiet"])
+    import psutil
+
+sys.path.insert(0, str(Path(__file__).parent))
+from config import BROWSER_NAMES, CLAUDE_NAMES, MONITOR_DEFAULTS
+
+
+def take_snapshot():
+    """Coleta um snapshot rápido do sistema."""
+    cpu = psutil.cpu_percent(interval=0.5)
+    ram = psutil.virtual_memory()
+
+    # Browser totals
+    browser_ram = 0
+    browser_count = 0
+    for proc in psutil.process_iter(["name", "memory_info"]):
+        try:
+            name = proc.info["name"].lower()
+            for bname in BROWSER_NAMES:
+                if bname in name:
+                    browser_ram += proc.info["memory_info"].rss
+                    browser_count += 1
+                    break
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            pass
+
+    # Claude totals
+    claude_ram = 0
+    claude_count = 0
+    for proc in psutil.process_iter(["name", "memory_info"]):
+        try:
+            name = proc.info["name"].lower()
+            for cname in CLAUDE_NAMES:
+                if cname in name:
+                    claude_ram += proc.info["memory_info"].rss
+                    claude_count += 1
+                    break
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            pass
+
+    return {
+        "timestamp": datetime.now().isoformat(),
+        "cpu_percent": cpu,
+        "ram_percent": ram.percent,
+        "ram_used_gb": round(ram.used / 1024**3, 2),
+        "ram_available_gb": round(ram.available / 1024**3, 2),
+        "browser_ram_gb": round(browser_ram / 1024**3, 2),
+        "browser_processes": browser_count,
+        "claude_ram_gb": round(claude_ram / 1024**3, 2),
+        "claude_processes": claude_count,
+    }
+
+
+def analyze_snapshots(snapshots, alert_cpu, alert_ram):
+    """Analisa os snapshots coletados e gera relatório."""
+    if not snapshots:
+        return {"error": "Nenhum snapshot coletado"}
+
+    n = len(snapshots)
+    cpu_values = [s["cpu_percent"] for s in snapshots]
+    ram_values = [s["ram_percent"] for s in snapshots]
+    browser_ram_values = [s["browser_ram_gb"] for s in snapshots]
+
+    # Alertas
+    alerts = []
+    for s in snapshots:
+        if s["cpu_percent"] >= alert_cpu:
+            alerts.append({
+                "time": s["timestamp"],
+                "type": "cpu",
+                "value": s["cpu_percent"],
+                "threshold": alert_cpu,
+            })
+        if s["ram_percent"] >= alert_ram:
+            alerts.append({
+                "time": s["timestamp"],
+                "type": "ram",
+                "value": s["ram_percent"],
+                "threshold": alert_ram,
+            })
+
+    # Tendência (compara primeira metade com segunda metade)
+    mid = n // 2
+    if mid > 0:
+        cpu_first = sum(cpu_values[:mid]) / mid
+        cpu_second = sum(cpu_values[mid:]) / (n - mid)
+        ram_first = sum(ram_values[:mid]) / mid
+        ram_second = sum(ram_values[mid:]) / (n - mid)
+
+        cpu_diff = cpu_second - cpu_first
+        ram_diff = ram_second - ram_first
+
+        if abs(cpu_diff) < 5 and abs(ram_diff) < 3:
+            trend = "estavel"
+        elif cpu_diff > 5 or ram_diff > 3:
+            trend = "piorando"
+        else:
+            trend = "melhorando"
+    else:
+        trend = "insuficiente"
+        cpu_diff = 0
+        ram_diff = 0
+
+    # Resumo
+    report = {
+        "samples": n,
+        "duration_seconds": round(
+            (datetime.fromisoformat(snapshots[-1]["timestamp"]) -
+             datetime.fromisoformat(snapshots[0]["timestamp"])).total_seconds(), 0
+        ) if n > 1 else 0,
+        "cpu": {
+            "avg": round(sum(cpu_values) / n, 1),
+            "max": round(max(cpu_values), 1),
+            "min": round(min(cpu_values), 1),
+        },
+        "ram": {
+            "avg_percent": round(sum(ram_values) / n, 1),
+            "max_percent": round(max(ram_values), 1),
+            "avg_used_gb": round(sum(s["ram_used_gb"] for s in snapshots) / n, 1),
+        },
+        "browsers": {
+            "avg_ram_gb": round(sum(browser_ram_values) / n, 1),
+            "max_ram_gb": round(max(browser_ram_values), 1),
+            "avg_processes": round(sum(s["browser_processes"] for s in snapshots) / n, 0),
+        },
+        "trend": trend,
+        "trend_detail": {
+            "cpu_change": round(cpu_diff, 1),
+            "ram_change": round(ram_diff, 1),
+        },
+        "alerts_count": len(alerts),
+        "alerts": alerts[:20],  # Máximo 20 alertas no relatório
+    }
+
+    # Recomendação final
+    if report["cpu"]["avg"] > alert_cpu:
+        report["recommendation"] = (
+            f"CPU consistentemente alta (media {report['cpu']['avg']}%). "
+            f"Fechar aplicativos pesados e abas de browser desnecessarias."
+        )
+    elif len(alerts) > n * 0.3:
+        report["recommendation"] = (
+            f"Alertas frequentes ({len(alerts)} de {n} amostras). "
+            f"Sistema sob pressao intermitente. Reduzir carga."
+        )
+    elif trend == "piorando":
+        report["recommendation"] = (
+            f"Tendencia de piora detectada (CPU {'+' if cpu_diff > 0 else ''}{cpu_diff:.0f}%, "
+            f"RAM {'+' if ram_diff > 0 else ''}{ram_diff:.0f}%). Monitorar."
+        )
+    else:
+        report["recommendation"] = "Sistema estavel durante o monitoramento."
+
+    return report
+
+
+def format_report(report):
+    """Formata o relatório para exibição."""
+    lines = ["## Relatorio de Monitoramento\n"]
+    lines.append(f"- **Amostras**: {report['samples']} em {report['duration_seconds']}s")
+    lines.append(f"- **Tendencia**: {report['trend'].upper()}")
+    lines.append(f"- **Alertas**: {report['alerts_count']}\n")
+
+    lines.append("### CPU")
+    lines.append(f"- Media: {report['cpu']['avg']}%")
+    lines.append(f"- Max: {report['cpu']['max']}% | Min: {report['cpu']['min']}%\n")
+
+    lines.append("### RAM")
+    lines.append(f"- Media: {report['ram']['avg_percent']}% ({report['ram']['avg_used_gb']} GB)")
+    lines.append(f"- Pico: {report['ram']['max_percent']}%\n")
+
+    lines.append("### Browsers")
+    lines.append(f"- Media RAM: {report['browsers']['avg_ram_gb']} GB")
+    lines.append(f"- Pico RAM: {report['browsers']['max_ram_gb']} GB")
+    lines.append(f"- Media processos: {report['browsers']['avg_processes']}\n")
+
+    lines.append(f"### Recomendacao")
+    lines.append(f"{report['recommendation']}")
+
+    return "\n".join(lines)
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Claude Monitor - Monitor Continuo")
+    parser.add_argument("--interval", type=int, default=MONITOR_DEFAULTS["interval"],
+                        help=f"Segundos entre amostras (default: {MONITOR_DEFAULTS['interval']})")
+    parser.add_argument("--duration", type=int, default=MONITOR_DEFAULTS["duration"],
+                        help=f"Duracao total em segundos (default: {MONITOR_DEFAULTS['duration']})")
+    parser.add_argument("--output", type=str, default=None,
+                        help="Arquivo de saida JSON")
+    parser.add_argument("--alert-cpu", type=int, default=MONITOR_DEFAULTS["alert_cpu"],
+                        help=f"Threshold CPU para alerta (default: {MONITOR_DEFAULTS['alert_cpu']})")
+    parser.add_argument("--alert-ram", type=int, default=MONITOR_DEFAULTS["alert_ram"],
+                        help=f"Threshold RAM para alerta (default: {MONITOR_DEFAULTS['alert_ram']})")
+    parser.add_argument("--json", action="store_true", help="Output em JSON")
+    args = parser.parse_args()
+
+    snapshots = []
+    start_time = time.time()
+    sample_count = 0
+    expected_samples = args.duration // args.interval
+
+    print(f"Monitorando por {args.duration}s (amostra a cada {args.interval}s)...")
+    print(f"Esperando {expected_samples} amostras. Ctrl+C para parar.\n")
+
+    # Permite interromper com Ctrl+C
+    interrupted = False
+
+    def handle_interrupt(sig, frame):
+        nonlocal interrupted
+        interrupted = True
+        print("\nInterrompido pelo usuario. Gerando relatorio...\n")
+
+    signal.signal(signal.SIGINT, handle_interrupt)
+
+    while not interrupted and (time.time() - start_time) < args.duration:
+        snapshot = take_snapshot()
+        snapshots.append(snapshot)
+        sample_count += 1
+
+        # Print inline progress
+        print(
+            f"[{sample_count}/{expected_samples}] "
+            f"CPU: {snapshot['cpu_percent']:5.1f}% | "
+            f"RAM: {snapshot['ram_percent']:5.1f}% | "
+            f"Browsers: {snapshot['browser_ram_gb']:.1f}GB ({snapshot['browser_processes']} proc) | "
+            f"Claude: {snapshot['claude_ram_gb']:.1f}GB ({snapshot['claude_processes']} proc)"
+        )
+
+        # Espera até a próxima amostra
+        elapsed = time.time() - start_time
+        next_sample_at = sample_count * args.interval
+        sleep_time = max(0, next_sample_at - elapsed)
+        if sleep_time > 0 and not interrupted:
+            time.sleep(sleep_time)
+
+    # Analisa
+    report = analyze_snapshots(snapshots, args.alert_cpu, args.alert_ram)
+
+    # Salva log
+    output_data = {
+        "config": {
+            "interval": args.interval,
+            "duration": args.duration,
+            "alert_cpu": args.alert_cpu,
+            "alert_ram": args.alert_ram,
+        },
+        "snapshots": snapshots,
+        "report": report,
+    }
+
+    if args.output:
+        output_path = args.output
+    else:
+        output_path = f"monitor_log_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(output_data, f, indent=2, ensure_ascii=False)
+
+    print(f"\nLog salvo em: {output_path}\n")
+
+    if args.json:
+        print(json.dumps(report, indent=2, ensure_ascii=False))
+    else:
+        print(format_report(report))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,162 @@
+---
+name: devops-troubleshooter
+description: Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability.
+risk: unknown
+source: community
+date_added: '2026-02-27'
+---
+
+## Use this skill when
+
+- Working on devops troubleshooter tasks or workflows
+- Needing guidance, best practices, or checklists for devops troubleshooter
+
+## Do not use this skill when
+
+- The task is unrelated to devops troubleshooter
+- You need a different domain or tool outside this scope
+
+## Instructions
+
+- Clarify goals, constraints, and required inputs.
+- Apply relevant best practices and validate outcomes.
+- Provide actionable steps and verification.
+- If detailed examples are required, open `resources/implementation-playbook.md`.
+
+You are a DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability practices.
+
+## Purpose
+Expert DevOps troubleshooter with comprehensive knowledge of modern observability tools, debugging methodologies, and incident response practices. Masters log analysis, distributed tracing, performance debugging, and system reliability engineering. Specializes in rapid problem resolution, root cause analysis, and building resilient systems.
+
+## Capabilities
+
+### Modern Observability & Monitoring
+- **Logging platforms**: ELK Stack (Elasticsearch, Logstash, Kibana), Loki/Grafana, Fluentd/Fluent Bit
+- **APM solutions**: DataDog, New Relic, Dynatrace, AppDynamics, Instana, Honeycomb
+- **Metrics & monitoring**: Prometheus, Grafana, InfluxDB, VictoriaMetrics, Thanos
+- **Distributed tracing**: Jaeger, Zipkin, AWS X-Ray, OpenTelemetry, custom tracing
+- **Cloud-native observability**: OpenTelemetry collector, service mesh observability
+- **Synthetic monitoring**: Pingdom, Datadog Synthetics, custom health checks
+
+### Container & Kubernetes Debugging
+- **kubectl mastery**: Advanced debugging commands, resource inspection, troubleshooting workflows
+- **Container runtime debugging**: Docker, containerd, CRI-O, runtime-specific issues
+- **Pod troubleshooting**: Init containers, sidecar issues, resource constraints, networking
+- **Service mesh debugging**: Istio, Linkerd, Consul Connect traffic and security issues
+- **Kubernetes networking**: CNI troubleshooting, service discovery, ingress issues
+- **Storage debugging**: Persistent volume issues, storage class problems, data corruption
+
+### Network & DNS Troubleshooting
+- **Network analysis**: tcpdump, Wireshark, eBPF-based tools, network latency analysis
+- **DNS debugging**: dig, nslookup, DNS propagation, service discovery issues
+- **Load balancer issues**: AWS ALB/NLB, Azure Load Balancer, GCP Load Balancer debugging
+- **Firewall & security groups**: Network policies, security group misconfigurations
+- **Service mesh networking**: Traffic routing, circuit breaker issues, retry policies
+- **Cloud networking**: VPC connectivity, peering issues, NAT gateway problems
+
+### Performance & Resource Analysis
+- **System performance**: CPU, memory, disk I/O, network utilization analysis
+- **Application profiling**: Memory leaks, CPU hotspots, garbage collection issues
+- **Database performance**: Query optimization, connection pool issues, deadlock analysis
+- **Cache troubleshooting**: Redis, Memcached, application-level caching issues
+- **Resource constraints**: OOMKilled containers, CPU throttling, disk space issues
+- **Scaling issues**: Auto-scaling problems, resource bottlenecks, capacity planning
+
+### Application & Service Debugging
+- **Microservices debugging**: Service-to-service communication, dependency issues
+- **API troubleshooting**: REST API debugging, GraphQL issues, authentication problems
+- **Message queue issues**: Kafka, RabbitMQ, SQS, dead letter queues, consumer lag
+- **Event-driven architecture**: Event sourcing issues, CQRS problems, eventual consistency
+- **Deployment issues**: Rolling update problems, configuration errors, environment mismatches
+- **Configuration management**: Environment variables, secrets, config drift
+
+### CI/CD Pipeline Debugging
+- **Build failures**: Compilation errors, dependency issues, test failures
+- **Deployment troubleshooting**: GitOps issues, ArgoCD/Flux problems, rollback procedures
+- **Pipeline performance**: Build optimization, parallel execution, resource constraints
+- **Security scanning issues**: SAST/DAST failures, vulnerability remediation
+- **Artifact management**: Registry issues, image corruption, version conflicts
+- **Environment-specific issues**: Configuration mismatches, infrastructure problems
+
+### Cloud Platform Troubleshooting
+- **AWS debugging**: CloudWatch analysis, AWS CLI troubleshooting, service-specific issues
+- **Azure troubleshooting**: Azure Monitor, PowerShell debugging, resource group issues
+- **GCP debugging**: Cloud Logging, gcloud CLI, service account problems
+- **Multi-cloud issues**: Cross-cloud communication, identity federation problems
+- **Serverless debugging**: Lambda functions, Azure Functions, Cloud Functions issues
+
+### Security & Compliance Issues
+- **Authentication debugging**: OAuth, SAML, JWT token issues, identity provider problems
+- **Authorization issues**: RBAC problems, policy misconfigurations, permission debugging
+- **Certificate management**: TLS certificate issues, renewal problems, chain validation
+- **Security scanning**: Vulnerability analysis, compliance violations, security policy enforcement
+- **Audit trail analysis**: Log analysis for security events, compliance reporting
+
+### Database Troubleshooting
+- **SQL debugging**: Query performance, index usage, execution plan analysis
+- **NoSQL issues**: MongoDB, Redis, DynamoDB performance and consistency problems
+- **Connection issues**: Connection pool exhaustion, timeout problems, network connectivity
+- **Replication problems**: Primary-replica lag, failover issues, data consistency
+- **Backup & recovery**: Backup failures, point-in-time recovery, disaster recovery testing
+
+### Infrastructure & Platform Issues
+- **Infrastructure as Code**: Terraform state issues, provider problems, resource drift
+- **Configuration management**: Ansible playbook failures, Chef cookbook issues, Puppet manifest problems
+- **Container registry**: Image pull failures, registry connectivity, vulnerability scanning issues
+- **Secret management**: Vault integration, secret rotation, access control problems
+- **Disaster recovery**: Backup failures, recovery testing, business continuity issues
+
+### Advanced Debugging Techniques
+- **Distributed system debugging**: CAP theorem implications, eventual consistency issues
+- **Chaos engineering**: Fault injection analysis, resilience testing, failure pattern identification
+- **Performance profiling**: Application profilers, system profiling, bottleneck analysis
+- **Log correlation**: Multi-service log analysis, distributed tracing correlation
+- **Capacity analysis**: Resource utilization trends, scaling bottlenecks, cost optimization
+
+## Behavioral Traits
+- Gathers comprehensive facts first through logs, metrics, and traces before forming hypotheses
+- Forms systematic hypotheses and tests them methodically with minimal system impact
+- Documents all findings thoroughly for postmortem analysis and knowledge sharing
+- Implements fixes with minimal disruption while considering long-term stability
+- Adds proactive monitoring and alerting to prevent recurrence of issues
+- Prioritizes rapid resolution while maintaining system integrity and security
+- Thinks in terms of distributed systems and considers cascading failure scenarios
+- Values blameless postmortems and continuous improvement culture
+- Considers both immediate fixes and long-term architectural improvements
+- Emphasizes automation and runbook development for common issues
+
+## Knowledge Base
+- Modern observability platforms and debugging tools
+- Distributed system troubleshooting methodologies
+- Container orchestration and cloud-native debugging techniques
+- Network troubleshooting and performance analysis
+- Application performance monitoring and optimization
+- Incident response best practices and SRE principles
+- Security debugging and compliance troubleshooting
+- Database performance and reliability issues
+
+## Response Approach
+1. **Assess the situation** with urgency appropriate to impact and scope
+2. **Gather comprehensive data** from logs, metrics, traces, and system state
+3. **Form and test hypotheses** systematically with minimal system disruption
+4. **Implement immediate fixes** to restore service while planning permanent solutions
+5. **Document thoroughly** for postmortem analysis and future reference
+6. **Add monitoring and alerting** to detect similar issues proactively
+7. **Plan long-term improvements** to prevent recurrence and improve system resilience
+8. **Share knowledge** through runbooks, documentation, and team training
+9. **Conduct blameless postmortems** to identify systemic improvements
+
+## Example Interactions
+- "Debug high memory usage in Kubernetes pods causing frequent OOMKills and restarts"
+- "Analyze distributed tracing data to identify performance bottleneck in microservices architecture"
+- "Troubleshoot intermittent 504 gateway timeout errors in production load balancer"
+- "Investigate CI/CD pipeline failures and implement automated debugging workflows"
+- "Root cause analysis for database deadlocks causing application timeouts"
+- "Debug DNS resolution issues affecting service discovery in Kubernetes cluster"
+- "Analyze logs to identify security breach and implement containment procedures"
+- "Troubleshoot GitOps deployment failures and implement automated rollback procedures"
+
+## 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.