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Found 356 Skills
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
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.
Migration monitoring, CDC, and observability infrastructure
Implement request logging, tracing, and observability. Use for debugging, monitoring, and production observability.
Use when you need to implement or improve Java logging and observability — including selecting SLF4J with Logback/Log4j2, applying proper log levels (ERROR, WARN, INFO, DEBUG, TRACE), parameterized logging, secure logging without sensitive data exposure, environment-specific configuration, log aggregation and monitoring, or validating logging through tests. This should trigger for requests such as Improve logging; Apply logging; Refactor logging; Add logging support. Part of cursor-rules-java project
OpenTelemetry observability patterns: traces, metrics, logs, context propagation, OTLP export, Collector pipelines, and troubleshooting
Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.
Set up monitoring, logging, and alerting for infrastructure and applications. Use when implementing observability, creating dashboards, or configuring alerts.
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.
Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging
Query Prometheus and Loki billing metrics from Grafana. Use when discussing observability costs, active series, ingestion rates, storage usage, or cardinality analysis.
Hookdeck Event Gateway — webhook infrastructure that replaces your queue. Use when receiving webhooks and need guaranteed delivery, automatic retries, replay, rate limiting, filtering, or observability. Eliminates the need for your own message queue for webhook processing.