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Found 323 Skills
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Setup Sentry Tracing (Performance Monitoring) in any project. Use when asked to enable tracing, track transactions/spans, measure latency, or add performance monitoring. Supports JavaScript, Python, and Ruby.
Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.
Distributed traces, spans, service dependencies, performance analysis, and failure detection. Query trace data, analyze request flows, and investigate span-level details.
Comprehensive guide and toolkit for diagnosing Rspack build issues. Quickly identify where crashes/errors occur, or perform detailed performance profiling to resolve bottlenecks. Use when the user encounters build failures, slow builds, or wants to optimize Rspack performance.
Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging
Implement distributed tracing with correlation IDs, trace propagation, and span tracking across microservices. Use when debugging distributed systems, monitoring request flows, or implementing observability.
Guidance for implementing path tracers and ray tracers to reconstruct or generate images. This skill applies when tasks involve writing C/C++ ray tracing code, reconstructing images from reference images, or building rendering systems with spheres, shadows, and procedural textures. Use for image reconstruction tasks requiring similarity matching.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.
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.
Adds OpenTelemetry-based tracing to applications via TrueFoundry's tracing platform (Traceloop SDK). Creates tracing projects, instruments Python/TypeScript code, and captures LLM calls and custom spans.
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.