Agent skill · data analytics · rmyndharis
distributed-tracing
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.
Why this skill is useful
Provides specific Jaeger and Tempo setup instructions along with application instrumentation examples that enhance observability in microservices.
What it needs
Requires docker, kubectl installed locally. About 4k tokens when loaded. Last updated 2026-08-02. 1,255 stars on the source repository.
What this skill does
Distributed Tracing Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices. Do not use this skill when The task is unrelated to distributed tracing You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. Purpose Track requests across distributed systems to understand latency, dependencies, and failure points. Use this skill when Debug latency issues Understand service dependencies Identify bottlenecks Trace error propagation Analyze request paths Distributed Tracing Concepts Trace Structure Key Components Trace - End-to-end request journey Span - Single operation within a trace Context - Metadata propagated between services Tags - Key-value pairs for filtering Logs - Timestamped events within a span Jaeger Setup Kubernetes Deployment Docker Compose Application Instrumentation OpenTelemetry (Recommended) Python (Flask) Node.js (Express) Go Context Propagation HTTP Headers Propagation in HTTP Requests Python Node.js Tempo Setup (Grafana) Kubernetes Deployment Sampling Strategies Probabilistic Sampling Rate Limiting Sampling Adaptive Sampling Trace Analysis Finding Slow Requests Jaeger Query: Finding Errors Jaeger Query: Service Dependency Graph Jaeger automatically generates service dependency graphs showing: Service relationships Request rates Error rates Average latencies Best Practices 1. Sample appropriately (1-10% in production) 2. Add meaningful tags (userid, requestid) 3. Propagate context across all service boundaries 4. Log exceptions in spans 5. Use consistent naming for operations 6. Monitor tracing overhead (<1% CPU impact) 7. Set up alerts for trace errors 8. Implement distributed context (baggage) 9. Use span events for important milestones 10. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills rmyndharis/distributed-tracing