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.
76
63%
Does it follow best practices?
Impact
100%
1.29xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/observability-monitoring/skills/distributed-tracing/SKILL.mdPython Flask OpenTelemetry instrumentation
OpenTelemetry imports
100%
100%
BatchSpanProcessor used
100%
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SERVICE_NAME resource attribute
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JaegerExporter agent host
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JaegerExporter agent port
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TracerProvider registration
100%
100%
FlaskInstrumentor applied
100%
100%
Context propagation inject
100%
100%
Meaningful span attributes
100%
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Error recording in spans
100%
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Manual span creation
100%
100%
Node.js Express tracing with context propagation
OpenTelemetry packages
100%
100%
NodeTracerProvider
20%
100%
service.name resource attribute
90%
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JaegerExporter HTTP endpoint
100%
100%
BatchSpanProcessor used
30%
100%
provider.register() called
25%
100%
registerInstrumentations called
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100%
HttpInstrumentation included
87%
100%
ExpressInstrumentation included
87%
100%
Context propagation inject
100%
100%
Meaningful span attributes
100%
100%
Docker Compose Jaeger setup with sampling and log correlation
Jaeger image
100%
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UI port exposed
100%
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Collector port exposed
100%
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Agent UDP port exposed
100%
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Zipkin port exposed
100%
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COLLECTOR_ZIPKIN_HOST_PORT env var
100%
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Probabilistic sampling rate
100%
100%
ParentBased TraceIdRatioBased sampler
0%
100%
trace_id in log records
100%
100%
trace_id 032x format
100%
100%
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