distributed-tracing
Helps your agent track how requests move between different system parts. It's useful for understanding data flow and finding issues in systems with many components.
What it does
Helps understand how requests travel through different parts of a complex software system. It shows the path of a request from start to finish, even if it goes through multiple services.
When to use it
- ✓When a request is taking too long to complete.
- ✓To find out where an error is happening in a multi-part system.
- ✓When connecting a new service to an existing system.
Example of what to ask your AI
“Show how a purchase request flows through the system, from cart to payment.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It works by recording information about each step of a request. This information is sent to a central place where it can be viewed as a map of the request's journey.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill distributed-tracingOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/distributed-tracing/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Distributed Tracing
When to Use
Trace a request across services, diagnose latency and error propagation, or add observable boundaries to a new integration.
Inputs and prerequisites
Record installed SDK and backend versions, permitted collector endpoints, the service graph and a staging request. Inspect the installed version's primary documentation before selecting exporter APIs or deployment configuration. This bundle does not install a tracing backend.
Procedure
- Read
resources/implementation-playbook.mdand identify one user journey. - Configure the matching OpenTelemetry SDK and OTLP exporter before loading instrumented frameworks. Use
references/instrumentation.mdfor propagation, shutdown and privacy checks. - Configure Jaeger using
references/jaeger-setup.md, or the existing Tempo/collector deployment's supported configuration. Review listeners, authentication, transport protection, storage and retention before any deployment. - Propagate context over HTTP and asynchronous messages. Use stable operation names and allowlisted attributes. Do not log credentials, raw request bodies or sensitive query values.
- Send successful and failing staging requests and verify connected spans in the backend. Record service identity, parentage, duration units and exporter errors.
- Measure queue loss and overhead. Select sampling based on those observations; no fixed percentage guarantees coverage or performance.
Example
A checkout calls inventory and payment. Verify that the root request and both downstream operations share a trace, that a simulated payment timeout is recorded, and that no card data is present. Repeat with exporter connectivity unavailable: request handling must retain the application's defined behavior.
Verification
- Actual trace evidence for each exercised boundary, including queue consumers.
- Allowed fields only; bounded attribute cardinality.
- Shutdown flush and exporter failure behavior observed.
- Access, storage and retention checked independently from UI availability.
Limitations
Head sampling can discard errors before tail sampling sees them. In-memory demos do not prove durable storage. Context propagation and cross-service clocks need actual integration tests; this guide is not a runnable multi-service application.
Sources
- OpenTelemetry exporter documentation — use the installed SDK's matching documentation.
FAQ
What are 'Jaeger' or 'Tempo'?
These are tools that help visualize request paths. Think of them as a map to understand the flow.
Do I need to install something on my computer?
This guide doesn't install the visualization tools (Jaeger/Tempo), but explains how to set up the system to send information to them.
Can I see sensitive data like passwords?
No, the system is configured to not record sensitive information like passwords or credit card details.
Details
Original description by the author
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2852
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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