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Found 491 Skills
Set up and use Dstl8 for observability. Triggers: install or configure Dstl8 (CLI, sources, MCP); incident triage and investigation; root cause analysis; checking whether a deploy fixed an issue; alerting on recurring patterns; cross-environment correlation; pre-coding context on past incidents and recent issues.
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.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Grafana Cloud cost management — usage monitoring, cost attribution by label, usage alerts, invoice management, and optimization strategies. Covers Adaptive Metrics (cardinality reduction), Adaptive Logs (log filtering), cost attribution labels, and the FOCUS-compliant billing application. Use when analyzing Grafana Cloud spending, setting up cost alerts, attributing costs to teams, reducing metric/log cardinality, or forecasting observability budgets.
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.
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"
eBPF skill for Linux observability and networking. Use when writing eBPF programs with libbpf or bpftrace, attaching kprobes/tracepoints/XDP hooks, debugging verifier errors, working with eBPF maps, or achieving CO-RE portability across kernel versions. Activates on queries about eBPF, bpftool, bpftrace, XDP programs, libbpf, verifier errors, eBPF maps, or kernel tracing with BPF.