correlation-tracing

Implement correlation IDs and distributed tracing to track requests across multiple services and understand system behavior.

aj-geddes/useful-ai-promptsUpdated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill correlation-tracing

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Installation Guide

How to use correlation-tracing on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add correlation-tracing
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill correlation-tracing

Fetches correlation-tracing from aj-geddes/useful-ai-prompts and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/correlation-tracing

Restart Cursor to activate correlation-tracing. Access via /correlation-tracing in your agent's command palette.

Security Notice

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.

Documentation

Correlation & Distributed Tracing

Table of Contents

Overview

Implement correlation IDs and distributed tracing to track requests across multiple services and understand system behavior.

When to Use

  • Microservices architectures
  • Debugging distributed systems
  • Performance monitoring
  • Request flow visualization
  • Error tracking across services
  • Dependency analysis
  • Latency optimization

Quick Start

Minimal working example:

import express from "express";
import { v4 as uuidv4 } from "uuid";

// Async local storage for context
import { AsyncLocalStorage } from "async_hooks";

const traceContext = new AsyncLocalStorage<Map<string, any>>();

interface TraceContext {
  traceId: string;
  spanId: string;
  parentSpanId?: string;
  serviceName: string;
}

function correlationMiddleware(serviceName: string) {
  return (
    req: express.Request,
    res: express.Response,
    next: express.NextFunction,
  ) => {
    // Extract or generate trace ID
    const traceId = (req.headers["x-trace-id"] as string) || uuidv4();
    const parentSpanId = req.headers["x-span-id"] as string;
    const spanId = uuidv4();
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Correlation ID Middleware (Express) Correlation ID Middleware (Express)
OpenTelemetry Integration OpenTelemetry Integration
Python Distributed Tracing Python Distributed Tracing
Manual Trace Propagation Manual Trace Propagation

Best Practices

✅ DO

  • Generate trace IDs at entry points
  • Propagate trace context across services
  • Include correlation IDs in logs
  • Use structured logging
  • Set appropriate span attributes
  • Sample traces in high-traffic systems
  • Monitor trace collection overhead
  • Implement context propagation

❌ DON'T

  • Skip trace propagation
  • Log without correlation context
  • Create too many spans
  • Store sensitive data in spans
  • Block on trace reporting
  • Forget error tracking

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.533 reviews
  • G
    Ganesh MohaneDec 28, 2024

    Solid pick for teams standardizing on skills: correlation-tracing is focused, and the summary matches what you get after install.

  • K
    Kiara SrinivasanDec 16, 2024

    correlation-tracing has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • S
    Sakshi PatilNov 19, 2024

    We added correlation-tracing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • K
    Kaira LopezNov 7, 2024

    correlation-tracing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • H
    Hana BrownOct 26, 2024

    We added correlation-tracing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • C
    Chaitanya PatilOct 10, 2024

    correlation-tracing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • M
    Maya RobinsonSep 17, 2024

    correlation-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • A
    Ama ReddySep 17, 2024

    I recommend correlation-tracing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • P
    Piyush GSep 1, 2024

    Registry listing for correlation-tracing matched our evaluation — installs cleanly and behaves as described in the markdown.

  • S
    Shikha MishraAug 20, 2024

    correlation-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.

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