Agent skill · data analytics · aiskillstore
distributed-tracing
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Why this skill is useful
Provides specific Jaeger and Tempo setup commands and application instrumentation examples that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, kubectl installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 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. If detailed examples are required, open resources/implementation-playbook.md. 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 Reference: See references/jaeger-setup.md Application Instrumentation OpenTelemetry (Recommended) Python (Flask) Node.js (Express) Go Reference: See references/instrumentation.md Context Propagation HTTP Headers Propagation in HTTP Requests Python Node.js Tempo Setup (Grafana) Kubernetes Deployment Reference: See assets/jaeger-config.yaml.template 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/distributed-tracing