sentry▌
tech-leads-club/agent-skills · updated May 23, 2026
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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.
| 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 asproject:read,event:read) or to log in and create one before running commands. - Set
SENTRY_AUTH_TOKENas an env var. - Optional defaults:
SENTRY_ORG,SENTRY_PROJECT,SENTRY_BASE_URL. - Defaults: org/project
{your-org}/{your-project}, time range24h, environmentprod, limit 20 (max 50). - Always call the Sentry API (no heuristics, no caching).
If the token is missing, give the user these steps:
- Create a Sentry auth token: https://sentry.io/settings/account/api/auth-tokens/
- Create a token with read-only scopes such as
project:read,event:read, andorg:read. - Set
SENTRY_AUTH_TOKENas an environment variable in their system. - 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: default24h(pass asstatsPeriod).environment: defaultprod.limit: default 20, max 50 (paginate until limit reached).search_query: optionalqueryparameter.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.
How to use sentry on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sentry from GitHub repository tech-leads-club/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sentry. Access the skill through slash commands (e.g., /sentry) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★73 reviews- ★★★★★Hassan Brown· Dec 28, 2024
Useful defaults in sentry — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yuki Martinez· Dec 24, 2024
Keeps context tight: sentry is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Valentina Chen· Dec 24, 2024
We added sentry from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mei Choi· Dec 16, 2024
sentry reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kiara Liu· Dec 16, 2024
Solid pick for teams standardizing on skills: sentry is focused, and the summary matches what you get after install.
- ★★★★★Shikha Mishra· Dec 4, 2024
sentry has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hassan Anderson· Dec 4, 2024
Registry listing for sentry matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zara Gill· Nov 23, 2024
Solid pick for teams standardizing on skills: sentry is focused, and the summary matches what you get after install.
- ★★★★★Hassan Thompson· Nov 19, 2024
I recommend sentry for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Li Johnson· Nov 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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