sentry
The Sentry skill retrieves and summarizes production errors from Sentry using read-only API access. Use it when a user asks to check Sentry issues, review recent crashes, generate production error reports, or inspect health data. It requires a valid SENTRY_AUTH_TOKEN with read-only scopes and handles API calls, pagination, and retries automatically through a bundled Python script.
git clone --depth 1 https://github.com/tech-leads-club/agent-skills /tmp/sentry && cp -r /tmp/sentry/packages/skills-catalog/skills/(monitoring)/sentry ~/.claude/skills/sentrySKILL.md
# 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)
```bash
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)
```bash
python3 "$SENTRY_API" \
list-issues \
--org {your-org} \
--project {your-project} \
--environment prod \
--time-range 24h \
--limit 20 \
--query "is:unresolved"
```
### 2) Resolve an issue short ID to issue ID
```bash
python3 "$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
```bash
python3 "$SENTRY_API" \
issue-detail \
1234567890
```
### 4) Issue events
```bash
python3 "$SENTRY_API" \
issue-events \
1234567890 \
--limit 20
```
### 5) Event detail (no stack traces by default)
```bash
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.Finds duplicate business logic spread across multiple components and suggests consolidation. Use when asking "where is this logic duplicated?", "find common code between services", "what can be consolidated?", "detect shared domain logic", or analyzing component overlap before refactoring. Do NOT use for code-level duplication detection (use linters) or dependency analysis (use coupling-analysis).
Detects misplaced classes and fixes component hierarchy problems — finds code that should belong inside a component but sits at the root level. Use when asking "clean up component structure", "find orphaned classes", "fix module hierarchy", "flatten nested components", or analyzing why namespaces have misplaced code. Do NOT use for dependency analysis (use coupling-analysis) or domain grouping (use domain-identification-grouping).
Maps architectural components in a codebase and measures their size to identify what should be extracted first. Use when asking "how big is each module?", "what components do I have?", "which service is too large?", "analyze codebase structure", "size my monolith", or planning where to start decomposing. Do NOT use for runtime performance sizing or infrastructure capacity planning.
Analyzes coupling between modules using the three-dimensional model (strength, distance, volatility) from "Balancing Coupling in Software Design". Use when asking "are these modules too coupled?", "show me dependencies", "analyze integration quality", "which modules should I decouple?", "coupling report", or evaluating architectural health. Do NOT use for domain boundary analysis (use domain-analysis) or component sizing (use component-identification-sizing).
Creates step-by-step decomposition plans and migration roadmaps for breaking apart monolithic applications. Use when asking "what order should I extract services?", "plan my migration", "create a decomposition roadmap", "prioritize what to split", "monolith to microservices strategy", or tracking decomposition progress. Do NOT use for domain analysis (use domain-analysis) or component sizing (use component-identification-sizing).
Maps business domains and suggests service boundaries in any codebase using DDD Strategic Design. Use when asking "what are the domains in this codebase?", "where should I draw service boundaries?", "identify bounded contexts", "classify subdomains", "DDD analysis", or analyzing domain cohesion. Do NOT use for grouping existing components into domains (use domain-identification-grouping) or dependency analysis (use coupling-analysis).
Groups existing components into logical business domains to plan service-based architecture. Use when asking "which components belong together?", "group these into services", "organize by domain", "component-to-domain mapping", or planning service extraction from an existing codebase. Do NOT use for identifying new domains from scratch (use domain-analysis) or analyzing coupling (use coupling-analysis).
AI frontend specialist and design consultant that guides users through a structured discovery process before generating any code. Collects visual references, design tokens, typography, icons, layout preferences, and brand guidelines to ensure the final output matches the user's vision with high fidelity. Use when the user asks to build, design, create, or improve any frontend interface — websites, landing pages, dashboards, components, apps, emails, forms, modals, or any UI element. Also triggers on "build me a UI", "design a page", "create a component", "improve this layout", "make this look better", "frontend", "interface", "redesign", or when the user provides mockups, screenshots, or design references. Do NOT use for backend logic, API design, database schemas, or non-visual code tasks.