distributed-tracing
🎯SkillA guide for following a single request as it passes through the many small services in your app, using Jaeger and Tempo. It helps you find where things slow down or break.
Installation
npx skills add wshobson/agents --skill distributed-tracingRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/distributed-tracingInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/distributed-tracing/ - All projects
~/.claude/skills/distributed-tracing/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
This skill helps you set up distributed tracing for apps made of many small services, often called microservices. Tracing records the path and timing of each request as it moves between services. It uses Jaeger and Tempo, two tools that collect and display these records.
Key Features
- Find slow steps and see which services depend on each other.
- Track how an error in one service spreads to others.
- Link your log messages to traces by adding a shared trace ID.
- Follow ten best practices, such as recording only 1-10% of requests in production.
- Fix common problems, like traces not showing up or tracing slowing your app.
Who is this for?
It suits backend developers and operations teams who debug apps split into many services. It also helps when you are adding monitoring to a growing system.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub →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.
Distributed Tracing
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Purpose
Track requests across distributed systems to understand latency, dependencies, and failure points.
When to Use
- Debug latency issues
- Understand service dependencies
- Identify bottlenecks
- Trace error propagation
- Analyze request paths
Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
- Sample appropriately (1-10% in production)
- Add meaningful tags (user_id, request_id)
- Propagate context across all service boundaries
- Log exceptions in spans
- Use consistent naming for operations
- Monitor tracing overhead (<1% CPU impact)
- Set up alerts for trace errors
- Implement distributed context (baggage)
- Use span events for important milestones
- Document instrumentation standards
Integration with Logging
Correlated Logs
import logging
from opentelemetry import trace
logger = logging.getLogger(__name__)
def process_request():
span = trace.get_current_span()
trace_id = span.get_span_context().trace_id
logger.info(
"Processing request",
extra={"trace_id": format(trace_id, '032x')}
)
Troubleshooting
No traces appearing:
- Check collector endpoint
- Verify network connectivity
- Check sampling configuration
- Review application logs
High latency overhead:
- Reduce sampling rate
- Use batch span processor
- Check exporter configuration
Related Skills
prometheus-configuration- For metricsgrafana-dashboards- For visualizationslo-implementation- For latency SLOs
More from this repository10
Helps you build a consistent look for your app with Tailwind CSS v4. It sets up shared colors, sizes, dark mode and reusable components so every screen matches.
A guide to TypeScript's advanced type features, which describe the shape of your data so mistakes are caught before the code runs. It covers reusable types, types that change based on rules, and built-in helper types, with short examples.
Guidance for building reliable server-side apps with Node.js, using Express or Fastify. It covers login, error handling, databases, API design and a list of proven best practices.
Intelligent automation and multi-agent orchestration for Claude Code
Claude Code plugin for AI-powered framework migration workflows, coordinating specialized agents to plan and execute transitions between technology stacks as part of the wshobson/agents marketplace.
A Claude Code plugin for payment system integration covering Stripe, PayPal, and billing platforms with checkout flows, subscription management, and webhook handling in the wshobson/agents ecosystem.
A Claude Code plugin bundling core development tools and agents for everyday tasks like debugging and code generation, part of the wshobson/agents marketplace with 73 focused plugins.
Claude Code plugin for observability and monitoring including logging, metrics, distributed tracing, and alerting, part of the wshobson/agents marketplace with 73 focused plugins.
A Claude Code plugin providing specialized AI agents for deployment strategy planning and execution, covering blue-green deployments, canary releases, rolling updates, and zero-downtime patterns.
Claude Code plugin for JVM language development including Java, Kotlin, and Scala with specialized agents for idiomatic patterns, build tools, and runtime optimization in the wshobson/agents marketplace.