Imported from tortastudios/sentry-instrumentation (SKILL.md). Install upstream with npx skills add tortastudios/sentry-instrumentation. Copyright stays with the author.

Sentry Instrumentation

Two surfaces: system metrics (counter / gauge / distribution, duration, failure, resource) and tracing (currently the gen_ai.* spans for AI agent conversations — see rule 7). Product-analytics events (clicks, funnels, flag exposure) belong in your product-analytics tool — never in Sentry. Python under examples/python/ is the canonical reference; other languages port the same shapes under idiomatic names.

Do not invoke for product-analytics changes. Stop and use the right tool.

Decision rules

1. New metric? Read references/signal-model.md and pick a classmethod constructor (MetricDef.counter|latency|gauge|resource|failure_counter). Register in the project's metric registry. Never call an emission helper with a raw string or a dynamically-assembled name.
2. Tag values? Either enumerate them in MetricDef.tag_constraints or route through a bucket function from references/tagging-and-cardinality.md.
3. Inside a loop? Use AggregatingCounter or DurationAccumulator (see references/cost-model.md). If the metric's loop_policy is "forbidden" the CI gate refuses any emission inside a for/while body for that metric.
4. New surface (HTTP route / external API / workflow step / retry / fallback)? Use the matching reusable pattern from references/surface-patterns.md. Don't hand-roll the emissions.
5. Changing a metric's meaning, unit, or tag shape? It's a new versioned metric. See references/naming-and-lifecycle.md.
6. Failure counter? Build with MetricDef.failure_counter(...) and emit with emit_failure(metric, failure=classify(exc), tags=...). Never pass str(exc) as a tag. See references/failure-taxonomy.md.
7. AI agent / LLM call / tool call / conversation? This is tracing, not metrics. Use gen_ai.* spans (invoke_agent / chat / execute_tool) and set gen_ai.conversation.id per turn so Sentry's Conversations view groups the session. See references/ai-agent-conversations.md and examples/python/ai_agent_spans.py. The conversation id and message bodies go on span attributes only — never on a metric tag (unbounded cardinality). Still emit a governed failure_counter on the failure path.

Language detection

Detect the project language from manifest files, then extend any existing observability layer you find (observability.py / observability.ts / metrics/ package). If none exists, scaffold from the matching example directory.

pyproject.toml / setup.py  → Python. Use examples/python/.
package.json               → TypeScript/JavaScript. Port from examples/python/ shapes.
go.mod                     → Go. Port from examples/python/ shapes.
Gemfile                    → Ruby. Port from examples/python/ shapes.
pom.xml / build.gradle     → Java/Kotlin. Port from examples/python/ shapes.

For ports: preserve the five constructors, the FailureClass taxonomy values, the 13 CI gate checks, and the emission-boundary rules. Names become idiomatic (emit_counter → emitCounter, @instrumented_step → instrumentedStep(fn), etc.).

Python project paths (canonical reference)

Replace yourapp with the project's package root on first use.

Emission module:    yourapp/observability.py
Registry:           yourapp/shared/metrics.py
Tag buckets:        yourapp/shared/metric_tags.py
Failure taxonomy:   yourapp/shared/failure_taxonomy.py
HTTP middleware:    yourapp/middleware/observability.py
Workflow decorator: yourapp/services/<workflow>/instrumentation.py
External API base:  yourapp/services/providers/instrumented_http_client.py
Retry helper:       yourapp/services/retry.py
Fallback helper:    yourapp/observability.py (or yourapp/shared/fallback.py)
AI agent spans:     yourapp/observability/ai_spans.py
CI gate:            scripts/check_metrics.py

References (load on demand)