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Found 472 Skills
Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.
Create, update, and manage Slot deployments for Katana and Torii services.
Microservices architecture patterns and best practices. Use when designing distributed systems, breaking down monoliths, or implementing service communication.
AI 도입 전략, Build vs Buy, 우선순위 설정, 거버넌스/보안, 6개월 확장 로드맵을 다루는 모듈.
Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first for current schemas.
Implements OpenTelemetry (OTEL) logging with trace context correlation and structured logging. Use when setting up production logging with OTEL exporters, structlog/loguru integration, trace context propagation, and comprehensive test patterns. Covers Python implementations for FastAPI, Kafka consumers, and background jobs. Includes OTLP, Jaeger, and console exporters.
Generate/create Loki configs — ingester, querier, compactor, ruler, S3/GCS/Azure backends.
Expert knowledge for Azure Monitor development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Monitor applications. Not for Azure Managed Grafana (use azure-managed-grafana), Azure Network Watcher (use azure-network-watcher), Azure Service Health (use azure-service-health), Azure Defender For Cloud (use azure-defender-for-cloud).
Lumigo integration. Manage data, records, and automate workflows. Use when the user wants to interact with Lumigo data.
Aggregate and display system metrics with anomaly detection for a time period
Guides Site Reliability Engineering—SLI/SLO and error budgets, reliability dashboards and burn-rate alerting, production readiness reviews, capacity planning for availability, toil reduction, dependency and failure-mode analysis, release reliability (canaries, rollback criteria), and service-owner incident mitigation tied to customer impact. Use when defining or operating SLOs, measuring error budget burn, improving service reliability, running PRRs before launch, planning scalable resilient capacity, or leading technical mitigation during outages—not for CI/CD pipeline implementation (devops), incident program and paging policy design (incident-management-engineer), cloud access and patch tickets (cloud-system-administrator), load-test profiling (performance-engineer), rollout cutover strategy (deployment-strategist), or greenfield cloud build-out (cloud-engineer).
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the LLM Obs SDK", or has `ddtrace` installed and wants idiomatic SDK code.