Track requests across microservices to identify latency, dependencies, and failure points.
Works with
Supports Jaeger and Tempo backends with OpenTelemetry instrumentation for Python, Node.js, and Go
Includes trace structure concepts (traces, spans, context, tags, logs) and automatic service dependency graph generation
Provides sampling strategies (probabilistic, rate-limiting, adaptive) to control tracing overhead in production
Covers context propagation via HTTP headers, trace analysis que
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondistributed-tracingExecute the skills CLI command in your project's root directory to begin installation:
Fetches distributed-tracing from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate distributed-tracing. Access via /distributed-tracing in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
33.1K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
33.1K
stars
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Track requests across distributed systems to understand latency, dependencies, and failure points.
Trace (Request ID: abc123)
↓
Span (frontend) [100ms]
↓
Span (api-gateway) [80ms]
├→ Span (auth-service) [10ms]
└→ Span (user-service) [60ms]
└→ Span (database) [40ms]
# Deploy Jaeger Operator
kubectl create namespace observability
kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml -n observability
# Deploy Jaeger instance
kubectl apply -f - <<EOF
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
name: jaeger
namespace: observability
spec:
strategy: production
storage:
type: elasticsearch
options:
es:
server-urls: http://elasticsearch:9200
ingress:
enabled: true
EOF
version: "3.8"
services:
jaeger:
image: jaegertracing/all-in-one:latest
ports:
- "5775:5775/udp"
- "6831:6831/udp"
- "6832:6832/udp"
- "5778:5778"
- "16686:16686" # UI
- "14268:14268" # Collector
- "14250:14250" # gRPC
- "9411:9411" # Zipkin
environment:
- COLLECTOR_ZIPKIN_HOST_PORT=:9411
Reference: See references/jaeger-setup.md
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from flask import Flask
# Initialize tracer
resource = Resource(attributes={SERVICE_NAME: "my-service"})
provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(JaegerExporter(
agent_host_name="jaeger",
agent_port=6831,
))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
# Instrument Flask
app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)
@app.route('/api/users')
def get_users():
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("get_users") as span:
span.set_attribute("user.count", 100)
# Business logic
users = fetch_users_from_db()
return {"users": users}
def fetch_users_from_db():
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("database_query") as span:
span.set_attribute("db.system", "postgresql")
span.set_attribute("db.statement", "SELECT * FROM users")
# Database query
return query_database()
const { NodeTracerProvider } = require("@opentelemetry/sdk-trace-node");
const { JaegerExporter } = require("@opentelemetry/exporter-jaeger");
const { BatchSpanProcessor } = require("@opentelemetry/sdk-trace-base");
const { registerInstrumentations } = require("@opentelemetry/instrumentation");
const { HttpInstrumentation } = require("@opentelemetry/instrumentation-http");
const {
ExpressInstrumentation,
} = require("@opentelemetry/instrumentation-express");
// Initialize tracer
const provider = new NodeTracerProvider({
resource: { attributes: { "service.name": "my-service" } },
});
const exporter = new JaegerExporter({
endpoint: "http://jaeger:14268/api/traces",
});
provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();
// Instrument libraries
registerInstrumentations({
instrumentations: [new HttpInstrumentation(), new ExpressInstrumentation()],
});
const express = require("express");
const app = express();
app.get("/api/users", async (req, res) => {
const tracer = trace.getTracer("my-service");
const span = tracer.startSpan("get_users");
try {
const users = await fetchUsers();
span.setAttributes({ "user.count": users.length });
res.json({ users });
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: distributed-tracing is focused, and the summary matches what you get after install.
distributed-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added distributed-tracing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend distributed-tracing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for distributed-tracing matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: distributed-tracing is focused, and the summary matches what you get after install.
Registry listing for distributed-tracing matched our evaluation — installs cleanly and behaves as described in the markdown.
distributed-tracing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend distributed-tracing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for distributed-tracing matched our evaluation — installs cleanly and behaves as described in the markdown.
showing 1-10 of 47