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Found 234 Skills
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.
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.
Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging
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.
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.
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
Implement distributed tracing with correlation IDs, trace propagation, and span tracking across microservices. Use when debugging distributed systems, monitoring request flows, or implementing observability.
Use when implementing distributed tracing, using Jaeger or Tempo, debugging microservices latency, or asking about "tracing", "Jaeger", "OpenTelemetry", "spans", "traces", "observability"
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 Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Guidance for reverse engineering graphics rendering programs (ray tracers, path tracers) from binary executables. This skill should be used when tasked with recreating a program that generates images through ray/path tracing, particularly when the goal is to achieve pixel-perfect or near-pixel-perfect output matching. Applies to tasks requiring binary analysis, floating-point constant extraction, and systematic algorithm reconstruction.