sentry-setup-metrics

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-metrics
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summary

Configure Sentry's custom metrics for tracking counters, gauges, and distributions.

skill.md

Setup Sentry Metrics

Configure Sentry's custom metrics for tracking counters, gauges, and distributions.

Invoke This Skill When

  • User asks to "add Sentry metrics" or "track custom metrics"
  • User wants counters, gauges, or distributions
  • User asks about Sentry.metrics or sentry_sdk.metrics

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

Check Sentry Metrics Getting Started for the full list of supported SDKs and minimum versions. Examples below use JavaScript and Python:

Platform Min SDK API Status
JavaScript 10.25.0+ Sentry.metrics.* Open Beta
Python 2.44.0+ sentry_sdk.metrics.* Open Beta
Ruby 6.3.0+ Sentry.metrics.* Open Beta

Metric Types

Type Purpose Example Use Cases
Counter Cumulative counts API calls, clicks, errors
Gauge Point-in-time values Queue depth, memory, connections
Distribution Statistical values Response times, cart amounts

JavaScript Setup

Metrics are enabled by default in SDK 10.25.0+.

Counter

Sentry.metrics.count("api_call", 1, {
  attributes: { endpoint: "/api/users", status_code: 200 },
});

Gauge

Sentry.metrics.gauge("queue_depth", 42, {
  unit: "none",
  attributes: { queue: "jobs" },
});

Distribution

Sentry.metrics.distribution("response_time", 187.5, {
  unit: "millisecond",
  attributes: { endpoint: "/api/products" },
});

Filtering (optional)

Sentry.init({
  beforeSendMetric: (metric) => {
    if (metric.attributes?.sensitive) return null;
    return metric;
  },
});

Python Setup

Metrics are enabled by default in SDK 2.44.0+.

Counter

sentry_sdk.metrics.count("api_call", 1, attributes={"endpoint": "/api/users"})

Gauge

sentry_sdk.metrics.gauge("queue_depth", 42, attributes={"queue": "jobs"})

Distribution

sentry_sdk.metrics.distribution(
    "response_time", 187.5,
    unit="millisecond",
    attributes={"endpoint": "/api/products"}
)

Filtering (optional)

def before_send_metric(metric, hint):
    if metric.get("attributes", {}).get("sensitive"):
        return None
    return metric

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

Common Units

Category Values
Time millisecond, second, minute, hour
Size byte, kilobyte, megabyte
Currency usd, eur, gbp
Other none, percent, ratio

Timing Helper Pattern

JavaScript

async function withTiming(name, fn, attrs = {}) {
  const start = performance.now();
  try { return await fn(); }
  finally {
    Sentry.metrics.distribution(name, performance.now() - start, {
      unit: "millisecond", attributes: attrs,
    });
  }
}

Python

import time, sentry_sdk

def track_duration(name, **attrs):
    def decorator(fn):
        def wrapper(*args, **kwargs):
            start = time.time()
            try: return fn(*args, **kwargs)
            finally:
                sentry_sdk.metrics.distribution(
                    name, (time.time() - start) * 1000,
                    unit="millisecond", attributes=attrs
                )
        return wrapper
    return decorator

Ruby Setup

Metrics are enabled by default in SDK 6.3.0+.

Counter

Sentry.metrics.count("api_call", 1, attributes: { endpoint: "/api/users" })

Gauge

Sentry.metrics.gauge("queue_depth", 42, attributes: { queue: "jobs" })

Distribution

Sentry.metrics.distribution("response_time", 187.5, unit: "millisecond", attributes: { endpoint: "/api/products" })

Best Practices

  • Stay under 2KB per metric: Each metric event has a 2KB size limit — keep attribute sets concise
  • Namespaced names: api.request.duration, not duration
  • Flush on exit: Call Sentry.flush() before process exit

Verification

After adding a metric, trigger the code path that emits it and check the Sentry Metrics dashboard (Explore > Metrics). Metrics may take a few minutes to appear due to buffer flushing.

Troubleshooting

Issue Solution
Metrics not appearing Verify SDK version, check DSN, wait for buffer flush
Metric dropped silently Check that metric event is under 2KB size limit — reduce attributes
Too many metrics Use beforeSendMetric to filter
how to use sentry-setup-metrics

How to use sentry-setup-metrics 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-metrics
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-metrics

The skills CLI fetches sentry-setup-metrics 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-metrics

Reload or restart Cursor to activate sentry-setup-metrics. Access the skill through slash commands (e.g., /sentry-setup-metrics) 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)
  • No comments yet — start the thread.
general reviews

Ratings

4.668 reviews
  • Ama Jain· Dec 28, 2024

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

  • Omar Dixit· Dec 28, 2024

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

  • Hana Abebe· Dec 20, 2024

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

  • Aanya Khan· Dec 16, 2024

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

  • Daniel Sanchez· Dec 4, 2024

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

  • Ishan Flores· Dec 4, 2024

    sentry-setup-metrics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Hana Mensah· Nov 23, 2024

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

  • Meera Perez· Nov 19, 2024

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

  • Naina Bansal· Nov 19, 2024

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

  • Harper Khanna· Nov 15, 2024

    sentry-setup-metrics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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