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Found 478 Skills
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
Create and configure Inngest durable functions. Covers triggers (events, cron, invoke), step execution and memoization, idempotency, cancellation, error handling, retries, logging, and observability.
Connect AI coding agents (Claude Code, Cursor, VS Code, OpenAI Codex) to Grafana Cloud via the Model Context Protocol (MCP) server. Use when the user asks to connect Claude Code to Grafana, set up MCP for Grafana, use Grafana tools in Cursor, query Grafana from an AI agent, configure the Grafana MCP server, or make AI agents interact with Grafana Cloud APIs. Triggers on phrases like "MCP server", "connect Claude Code to Grafana", "Grafana MCP", "AI agent Grafana", "Claude Grafana tools", "Cursor Grafana", or "agent observability".
Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.
Guidelines for building production-grade microservices with FastAPI/Python and Go, covering serverless patterns, clean architecture, observability, and resilience.
Grafana Cloud testing capabilities — Synthetic Monitoring (probing URLs, DNS, TCP, ping from multiple regions), k6 Cloud (managed load testing with distributed execution), and Frontend Observability (Faro, real user monitoring). Use when setting up uptime checks, external probes, configuring k6 cloud runs, monitoring frontend performance, or testing APIs from multiple locations.
Implement service mesh (Istio, Linkerd) for service-to-service communication, traffic management, security, and observability.
Application monitoring and observability setup for Python/React projects. Use when configuring logging, metrics collection, health checks, alerting rules, or dashboard creation. Covers structured logging with structlog, Prometheus metrics for FastAPI, health check endpoints, alert threshold design, Grafana dashboard patterns, error tracking with Sentry, and uptime monitoring. Does NOT cover incident response procedures (use incident-response) or deployment (use deployment-pipeline).
Create a new built-in evlog adapter to send wide events to an external observability platform. Use when adding a new drain adapter (e.g., for Datadog, Sentry, Loki, Elasticsearch, etc.) to the evlog package. Covers source code, build config, package exports, tests, and all documentation.
Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"