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Found 456 Skills
Use when building comprehensive monitoring and observability systems.
Set up orq.ai observability for LLM applications. Use when setting up tracing, adding the AI Router proxy, integrating OpenTelemetry, auditing existing instrumentation, or enriching traces with metadata.
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.
Vercel observability for Web Analytics, Speed Insights, logs, tracing, alerts, and observability tooling. Use when monitoring performance or debugging production behavior on Vercel.
You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da
Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
Migrate a Java application from the classic Elastic APM Java agent to the EDOT Java agent. Use when switching from elastic-apm-agent.jar to elastic-otel-javaagent.jar.
Apply when making VTEX IO services easier to observe, troubleshoot, and operate in production. Covers metrics, structured logging, failure visibility, rate-limit awareness, and production readiness checks for backend apps. Use for integration monitoring, error diagnosis, or improving the operational quality of VTEX IO services before or after release.
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
Migration monitoring, CDC, and observability infrastructure
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.