sentry-setup-tracing

getsentry/sentry-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/getsentry/sentry-agent-skills --skill sentry-setup-tracing
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summary

Configure Sentry's performance monitoring to track transactions and spans.

skill.md

Setup Sentry Tracing

Configure Sentry's performance monitoring to track transactions and spans.

Invoke This Skill When

  • User asks to "enable tracing" or "add performance monitoring"
  • User wants to track API response times, page loads, or latency
  • User asks about tracesSampleRate or custom spans

Important: The SDK versions, API names, and code samples below are examples. Always verify against docs.sentry.io before implementing, as APIs and minimum versions may have changed.

Quick Reference

Platform Min SDK Enable Custom Span
JS/Browser 9.0.0+ tracesSampleRate + browserTracingIntegration() Sentry.startSpan()
Next.js 9.0.0+ tracesSampleRate in each runtime config file Sentry.startSpan()
Node.js 9.0.0+ tracesSampleRate Sentry.startSpan()
Python 0.11.2+ traces_sample_rate @sentry_sdk.trace or start_span()
Ruby 5.0.0+ traces_sample_rate Sentry.with_child_span()

JavaScript Setup

Enable tracing

Sentry.init({
  dsn: "YOUR_DSN",
  tracesSampleRate: 1.0,  // 1.0 = 100%, lower for production
  integrations: [Sentry.browserTracingIntegration()],  // Browser/React only
  tracePropagationTargets: ["localhost", /^https:\/\/api\./],
});

Custom spans

// Async operation
const result = await Sentry.startSpan(
  { name: "fetch-user", op: "http.client" },
  async () => {
    return await fetch("/api/user").then(r => r.json());
  }
);

// Nested spans
await Sentry.startSpan({ name: "checkout", op: "transaction" }, async () => {
  await Sentry.startSpan({ name: "validate", op: "validation" }, validateCart);
  await Sentry.startSpan({ name: "payment", op: "payment" }, processPayment);
});

Dynamic sampling

tracesSampler: ({ name, inheritOrSampleWith }) => {
  if (name.includes("healthcheck")) return 0;
  if (name.includes("checkout")) return 1.0;
  return inheritOrSampleWith(0.1);  // Respects parent sampling decision, falls back to 0.1
},

Python Setup

Enable tracing

sentry_sdk.init(
    dsn="YOUR_DSN",
    traces_sample_rate=1.0,
)

Custom spans

# Decorator
@sentry_sdk.trace
def expensive_function():
    return do_work()

# Context manager
with sentry_sdk.start_span(name="process-order", op="task") as span:
    span.set_data("order.id", order_id)
    process(order_id)

Dynamic sampling

from sentry_sdk.types import SamplingContext

def traces_sampler(sampling_context: SamplingContext) -> float:
    name = sampling_context.get("transaction_context", {}).get("name", "")
    parent_sampled = sampling_context.get("parent_sampled")
    if "healthcheck" in name: return 0
    if "checkout" in name: return 1.0
    if parent_sampled is not None: return float(parent_sampled)  # Respect parent decision
    return 0.1

sentry_sdk.init(dsn="YOUR_DSN", traces_sampler=traces_sampler)

Ruby Setup

Sentry.init do |config|
  config.dsn = "YOUR_DSN"
  config.traces_sample_rate = 1.0
end

Common Operation Types

op Value Use Case
http.client Outgoing HTTP
http.server Incoming HTTP
db / db.query Database
cache Cache operations
queue.task Background jobs
function Function calls

Sampling Recommendations

Traffic Rate
Development 1.0
Low (<1K req/min) 0.5 - 1.0
Medium (1K-10K) 0.1 - 0.5
High (>10K) 0.01 - 0.1

Distributed Tracing

Configure tracePropagationTargets to send trace headers to your APIs:

tracePropagationTargets: ["localhost", "https://api.yourapp.com"],

For Next.js 14 App Router, add to root layout (not needed in Next.js 15+):

export function generateMetadata(): Metadata {
  return { other: { ...Sentry.getTraceData() } };
}

Verification

After enabling tracing, trigger a traced operation (e.g., an HTTP request) and check the Sentry Performance dashboard for transactions. Custom spans should appear nested under the parent transaction.

Troubleshooting

Issue Solution
Transactions not appearing Check tracesSampleRate > 0, verify DSN
Browser traces missing Add browserTracingIntegration()
Distributed traces disconnected Check tracePropagationTargets, CORS headers
Too many transactions Lower sample rate, use tracesSampler to filter
how to use sentry-setup-tracing

How to use sentry-setup-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 development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add sentry-setup-tracing
2

Execute installation command

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

$npx skills add https://github.com/getsentry/sentry-agent-skills --skill sentry-setup-tracing

The skills CLI fetches sentry-setup-tracing from GitHub repository getsentry/sentry-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/sentry-setup-tracing

Reload or restart Cursor to activate sentry-setup-tracing. Access the skill through slash commands (e.g., /sentry-setup-tracing) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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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

Installation Steps

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

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.552 reviews
  • Layla Gonzalez· Dec 28, 2024

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

  • Shikha Mishra· Dec 24, 2024

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

  • Ganesh Mohane· Dec 20, 2024

    sentry-setup-tracing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Kofi Chen· Dec 16, 2024

    Useful defaults in sentry-setup-tracing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kabir Lopez· Dec 16, 2024

    sentry-setup-tracing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Layla Desai· Dec 8, 2024

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

  • Layla Torres· Nov 19, 2024

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

  • Sakshi Patil· Nov 11, 2024

    Keeps context tight: sentry-setup-tracing is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Layla Flores· Nov 7, 2024

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

  • Sakura Menon· Nov 7, 2024

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

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