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Found 337 Skills
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.
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.
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.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Generate AgentforcePlatformTracingSettings metadata to enable or disable Agentforce agent execution trace spans flowing to Data Cloud. Use this skill for any AgentforcePlatformTracingSettings metadata work. TRIGGER when: user mentions Agentforce tracing, agent trace spans, Data Cloud tracing, AgentforcePlatformTracingSettings, platform observability tracing, enable agent tracing, wants agent execution spans in Data Cloud, mentions .settings-meta.xml for AgentforcePlatformTracing, or asks about enabling observability for Agentforce agents. DO NOT TRIGGER when: user wants Platform Tracing for TraceSpanEvent (use platform-tracing-configure), wants to query or analyze existing agent trace data in Data Cloud (use agentforce-observe), wants Event Log Files or ELF configuration, wants Change Data Capture (use integration-eventing-cdc-configure), or wants ManagedEventSubscription (use integration-eventing-subscription-configure).
Distributed traces, spans, service dependencies, performance analysis, and failure detection. Query trace data, analyze request flows, and investigate span-level details.
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.
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.
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"
Systematically trace bugs backward through call stack to find original trigger. Use when errors occur deep in execution and you need to trace back to find the original trigger.
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.
Use when implementing distributed tracing, using Jaeger or Tempo, debugging microservices latency, or asking about "tracing", "Jaeger", "OpenTelemetry", "spans", "traces", "observability"