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Found 277 Skills
Comprehensive toolkit for generating best practice PromQL (Prometheus Query Language) queries following current standards and conventions. Use this skill when creating new PromQL queries, implementing monitoring and alerting rules, or building observability dashboards.
Scaffold a production-ready Go HTTP service with OpenTelemetry observability, TLS, lifecycle management, Dockerfile, GitHub Actions CI/CD, and golangci-lint. Use when creating or regenerating a full Go service skeleton (project layout, config package, server package, CI workflows, and container build files).
Monitor use when you need to work with monitoring and observability. This skill provides health monitoring and alerting with comprehensive guidance and automation. Trigger with phrases like "monitor system health", "set up alerts", or "track metrics".
Comprehensive LLM audit. Model currency, prompt quality, evals, observability, CI/CD. Ensures all LLM-powered features follow best practices and are properly instrumented. Auto-invoke when: model names/versions mentioned, AI provider config, prompt changes, .env with AI keys, aiProviders.ts or prompts.ts modified, AI-related PRs. CRITICAL: Training data lags months. ALWAYS web search before LLM decisions.
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
Make application behavior visible to coding agents by exposing structured logs and telemetry. Use when asked to "add telemetry", "make logs accessible to agents", "add observability", "debug with logs", or when an agent needs to understand runtime behavior but has no way to query logs. Also use when debugging is difficult because there are no structured logs, when agent docs (CLAUDE.md, AGENTS.md) lack instructions for querying application logs, or when setting up logging infrastructure for a new or existing web application.
Implement distributed tracing using logs, including trace context propagation, span logging, correlation IDs, and OpenTelemetry integration for observability
AWS/GCP cloud infrastructure: Well-Architected, security, cost, observability. Use when working with Terraform outputs, IAM policies, VPC design, load balancers, or cloud architecture decisions.
Envoy Gateway production deployment — deployment modes, performance tuning, observability, operational guidance
Debug and troubleshoot production issues on Azure. Covers Container Apps diagnostics, log analysis with KQL, health checks, and common issue resolution for image pulls, cold starts, and health probes. USE FOR: debug production issues, troubleshoot container apps, analyze logs with KQL, fix image pull failures, resolve cold start issues, investigate health probe failures, check resource health, view application logs, find root cause of errors DO NOT USE FOR: deploying applications (use azure-deploy), creating new resources (use azure-prepare), setting up monitoring (use azure-observability), cost optimization (use azure-cost-optimization)
Instrument web applications to send telemetry data to Azure Application Insights for observability and monitoring. USE FOR: instrument app with app insights, add appinsights instrumentation, configure application insights, set up telemetry monitoring, enable app insights auto-instrumentation, add observability to azure web app, instrument webapp to send data to app insights, configure telemetry for app service. DO NOT USE FOR: non-Azure monitoring (use CloudWatch for AWS, Datadog for third-party), log analysis (use azure-kusto), cost monitoring (use azure-cost-optimization), security monitoring (use azure-security).