DevOps & Infra Skill
Bash Defensive Patterns
Master defensive Bash programming techniques for production-grade scripts.
$15
Distributed Tracing
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and…
Implements distributed tracing with Jaeger and Tempo so you can follow a single request across all your microservices and pinpoint where latency and failures occur. It covers OpenTelemetry instrumentation, context propagation, sampling strategy, and correlation of logs, metrics, and traces. Turns a foggy 'which service is slow?' question into a clear, visualized request journey.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category DevOps & Infra
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
The instrumentation sequence the skill executes to make a request visible across every microservice it touches:
- Stands up the tracing backend first: Jaeger via the Kubernetes operator or Docker Compose for smaller setups, or Grafana Tempo with object-storage backed trace retention.
- Instruments each service with OpenTelemetry: a TracerProvider with the service name resource, a BatchSpanProcessor exporting to the collector, and auto-instrumentation for the framework (Flask, Express, Go HTTP) so baseline spans appear without touching business code.
- Shapes the span hierarchy deliberately: parent spans per operation, child spans for database and downstream calls, attributes like db.system and db.statement for filtering, errors recorded on the span, and tree depth kept to 3 to 5 levels so the trace view stays readable.
- Propagates context across every boundary: W3C traceparent headers injected into HTTP calls, interceptors for gRPC, and trace context carried in message attributes for Kafka or RabbitMQ, because a single broken handoff fragments the entire trace.
- Sets a sampling strategy that survives production traffic: head-based 1 percent ratio sampling as the baseline, rate limiting at 100 traces per second against spikes, and tail-based sampling in the collector so error and high-latency traces are kept at 100 percent.
- Correlates the three pillars: trace_id written into every log line and attached to metrics as exemplars, so an alert jumps from a latency histogram to the exact trace to the exact logs in clicks instead of hours.
One power source. 6 lines out.
distributed-tracing · core
core active · 6 lines
- ✓ debugging latency issues
Debugging latency issues across a microservices architecture
- ✓ understanding service de…
Understanding service dependencies and request flow
- ✓ identifying performance…
Identifying performance bottlenecks in distributed systems
- ✓ tracing error propagation
Tracing error propagation from frontend to database
- ✓ instrumenting python, no…
Instrumenting Python, Node.js, or Go services with OpenTelemetry
- ✓ correlating logs, metrics
Correlating logs, metrics, and traces by trace ID in Grafana
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Cut incident resolution from hours to minutes with end-to-end request visibility
license: perpetual -
Pinpoint the exact service and operation causing latency
license: perpetual -
Control observability cost with smart head and tail sampling
license: perpetual -
See the full story of any request by correlating logs, metrics, and traces
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
Jaeger Kubernetes and Docker Compose deployment configs
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
Backend and platform engineers running microservices who need to trace requests end to end and find bottlenecks fast.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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We already use a different observability backend. Is this locked to Jaeger and Tempo?
The instrumentation is OpenTelemetry, which is vendor-neutral, so the traces can go to other compatible backends. Jaeger and Tempo are the worked examples, not a hard requirement.
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Will tracing really show me where latency is, or just add noise to my logs?
Context propagation ties one request across every service into a single trace, so a slow hop is visible instead of guessed. Sampling keeps the volume sane so you get signal, not a flood.
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I run a single service, not microservices. Is this worth setting up?
The payoff comes from following a request across service boundaries, which a monolith does not have. On one service, simpler request logging or profiling usually answers the same question with less work.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.