61ec71c5c7
New skills covering 10 categories: **Security & Audit**: 007 (STRIDE/PASTA/OWASP), cred-omega (secrets management) **AI Personas**: Karpathy, Hinton, Sutskever, LeCun (4 sub-skills), Altman, Musk, Gates, Jobs, Buffett **Multi-agent Orchestration**: agent-orchestrator, task-intelligence, multi-advisor **Code Analysis**: matematico-tao (Terence Tao-inspired mathematical code analysis) **Social & Messaging**: Instagram Graph API, Telegram Bot, WhatsApp Cloud API, social-orchestrator **Image Generation**: AI Studio (Gemini), Stability AI, ComfyUI Gateway, image-studio router **Brazilian Domain**: 6 auction specialist modules, 2 legal advisors, auctioneers data scraper **Product & Growth**: design, invention, monetization, analytics, growth engine **DevOps & LLM Ops**: Docker/CI-CD/AWS, RAG/embeddings/fine-tuning **Skill Governance**: installer, sentinel auditor, context management Each skill includes: - Standardized YAML frontmatter (name, description, risk, source, tags, tools) - Structured sections (Overview, When to Use, How it Works, Best Practices) - Python scripts and reference documentation where applicable - Cross-platform compatibility (Claude Code, Antigravity, Cursor, Gemini CLI, Codex CLI) Co-authored-by: ProgramadorBrasil <214873561+ProgramadorBrasil@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
70 lines
1.5 KiB
Python
70 lines
1.5 KiB
Python
"""
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Configurações e thresholds para o Claude Monitor.
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"""
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# Thresholds de alerta
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THRESHOLDS = {
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"cpu": {
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"ok": 60,
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"warning": 85,
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# acima de warning = critical
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},
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"ram_percent": {
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"ok": 70,
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"warning": 85,
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},
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"browsers_ram_gb": {
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"ok": 3.0,
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"warning": 6.0,
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},
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"browsers_processes": {
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"ok": 30,
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"warning": 60,
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},
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"disk_free_percent": {
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"critical_below": 10,
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"warning_below": 15,
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},
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"network_latency_ms": {
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"ok": 200,
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"warning": 500,
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},
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}
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# Nomes de processos de browser conhecidos
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BROWSER_NAMES = ["chrome", "msedge", "firefox", "brave", "opera", "vivaldi"]
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# Nomes de processos do Claude Code
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CLAUDE_NAMES = ["claude"]
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# Endpoint para teste de latência
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API_ENDPOINT = "api.anthropic.com"
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# Monitor defaults
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MONITOR_DEFAULTS = {
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"interval": 30,
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"duration": 300,
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"alert_cpu": 80,
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"alert_ram": 85,
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}
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def classify(value, metric_name):
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"""Classifica um valor como 'ok', 'warning' ou 'critical'."""
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t = THRESHOLDS.get(metric_name, {})
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# Métricas onde "abaixo" é ruim (disco livre)
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if "critical_below" in t:
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if value < t["critical_below"]:
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return "critical"
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elif value < t["warning_below"]:
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return "warning"
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return "ok"
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# Métricas onde "acima" é ruim (CPU, RAM, latência)
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if value <= t.get("ok", 999999):
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return "ok"
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elif value <= t.get("warning", 999999):
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return "warning"
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return "critical"
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