sentry

Inspect Sentry issues, summarize production errors, and pull health data via the Sentry API (read-only). Use when user says "check Sentry", "what errors in production?", "summarize Sentry issues", "recent crashes", or "production error report". Requires SENTRY_AUTH_TOKEN. Do NOT use for setting up Sentry SDK, configuring alerts, or non-Sentry error monitoring.

tech-leads-club/agent-skillsUpdated May 23, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/tech-leads-club/agent-skills --skill sentry

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Installation Guide

How to use sentry 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 sentry
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/tech-leads-club/agent-skills --skill sentry

Fetches sentry from tech-leads-club/agent-skills 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/sentry

Restart Cursor to activate sentry. Access via /sentry 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

name
sentry
description
Inspect Sentry issues, summarize production errors, and pull health data via the Sentry API (read-only). Use when user says "check Sentry", "what errors in production?", "summarize Sentry issues", "recent crashes", or "production error report". Requires SENTRY_AUTH_TOKEN. Do NOT use for setting up Sentry SDK, configuring alerts, or non-Sentry error monitoring.
metadata
author: github.com/openai/skills version: '1.0.0'

Sentry (Read-only Observability)

Quick start

  • If not already authenticated, ask the user to provide a valid SENTRY_AUTH_TOKEN (read-only scopes such as project:read, event:read) or to log in and create one before running commands.
  • Set SENTRY_AUTH_TOKEN as an env var.
  • Optional defaults: SENTRY_ORG, SENTRY_PROJECT, SENTRY_BASE_URL.
  • Defaults: org/project {your-org}/{your-project}, time range 24h, environment prod, limit 20 (max 50).
  • Always call the Sentry API (no heuristics, no caching).

If the token is missing, give the user these steps:

  1. Create a Sentry auth token: https://sentry.io/settings/account/api/auth-tokens/
  2. Create a token with read-only scopes such as project:read, event:read, and org:read.
  3. Set SENTRY_AUTH_TOKEN as an environment variable in their system.
  4. Offer to guide them through setting the environment variable for their OS/shell if needed.
  • Never ask the user to paste the full token in chat. Ask them to set it locally and confirm when ready.

Core tasks (use bundled script)

Use scripts/sentry_api.py for deterministic API calls. It handles pagination and retries once on transient errors.

Skill path (set once)

export AGENT_SKILLS_HOME="${AGENT_SKILLS_HOME:-$HOME/.agent-skills}"
export SENTRY_API="$AGENT_SKILLS_HOME/skills/sentry/scripts/sentry_api.py"

User-scoped skills install under $AGENT_SKILLS_HOME/skills (default: ~/.agent-skills/skills).

1) List issues (ordered by most recent)

python3 "$SENTRY_API" \
  list-issues \
  --org {your-org} \
  --project {your-project} \
  --environment prod \
  --time-range 24h \
  --limit 20 \
  --query "is:unresolved"

2) Resolve an issue short ID to issue ID

python3 "$SENTRY_API" \
  list-issues \
  --org {your-org} \
  --project {your-project} \
  --query "ABC-123" \
  --limit 1

Use the returned id for issue detail or events.

3) Issue detail

python3 "$SENTRY_API" \
  issue-detail \
  1234567890

4) Issue events

python3 "$SENTRY_API" \
  issue-events \
  1234567890 \
  --limit 20

5) Event detail (no stack traces by default)

python3 "$SENTRY_API" \
  event-detail \
  --org {your-org} \
  --project {your-project} \
  abcdef1234567890

API requirements

Always use these endpoints (GET only):

  • List issues: /api/0/projects/{org_slug}/{project_slug}/issues/
  • Issue detail: /api/0/issues/{issue_id}/
  • Events for issue: /api/0/issues/{issue_id}/events/
  • Event detail: /api/0/projects/{org_slug}/{project_slug}/events/{event_id}/

Inputs and defaults

  • org_slug, project_slug: default to {your-org}/{your-project} (avoid non-prod orgs).
  • time_range: default 24h (pass as statsPeriod).
  • environment: default prod.
  • limit: default 20, max 50 (paginate until limit reached).
  • search_query: optional query parameter.
  • issue_short_id: resolve via list-issues query first.

Output formatting rules

  • Issue list: show title, short_id, status, first_seen, last_seen, count, environments, top_tags; order by most recent.
  • Event detail: include culprit, timestamp, environment, release, url.
  • If no results, state explicitly.
  • Redact PII in output (emails, IPs). Do not print raw stack traces.
  • Never echo auth tokens.

Golden test inputs

  • Org: {your-org}
  • Project: {your-project}
  • Issue short ID: {ABC-123}

Example prompt: “List the top 10 open issues for prod in the last 24h.” Expected: ordered list with titles, short IDs, counts, last seen.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.773 reviews
  • H
    Hassan BrownDec 28, 2024

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

  • Y
    Yuki MartinezDec 24, 2024

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

  • V
    Valentina ChenDec 24, 2024

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

  • M
    Mei ChoiDec 16, 2024

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

  • K
    Kiara LiuDec 16, 2024

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

  • S
    Shikha MishraDec 4, 2024

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

  • H
    Hassan AndersonDec 4, 2024

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

  • Z
    Zara GillNov 23, 2024

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

  • H
    Hassan ThompsonNov 19, 2024

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

  • L
    Li JohnsonNov 19, 2024

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

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