ra-skills

<skills>

Write Dagger modules and functions in Go for container builds, CI/CD pipelines, and workflow automation. Use when: dagger, dagger module, dagger function, dagger call, dagger shell, dagger init, CI/CD pipeline as code, container build with dagger, publish container image, dagger SDK, daggerverse, dagger toolchain, dagger Go SDK, programmable CI, build-test-push pipeline, dagger.json, dag.Container, dag.Directory, multi-stage container build, dagger cloud, trivy scan, SBOM generation, vulnerability scanning, provenance attestation, cosign signing, supply chain security, docker compose dagger, service binding, dagger-for-github action, publish docker registry, publish pypi. Also use when the user wants to replace shell scripts or YAML CI configs with typed, composable Go code that runs identically locally and in CI.

Author production dockerfiles. Use when adding a new containerized image, modifying a *.dockerfile, debugging a slow/large build, or reviewing a dockerfile for security and cache efficiency. Enforces the .docker/<name>.dockerfile + repo-root build-context contract (the RA/rask convention) consumed by the dagger build system.

FastAPI best practices, conventions, and production project templates. Use when writing or refactoring FastAPI APIs and Pydantic models, or when scaffolding a new FastAPI project with async patterns, dependency injection, repositories, services, auth, and tests.

OpenTelemetry for Python services in this project — SDK setup, auto-instrumentation, custom spans/metrics/logs, semantic conventions, and the Collector pipeline that consumes the data. Use when instrumenting a Python service with traces/metrics/logs, picking attribute names, debugging missing telemetry, or deciding what belongs in the SDK vs the Collector.

Python patterns for system reliability — background jobs and task queues (NATS JetStream via nats-py), durable multi-step workflows (Dapr Workflow via dapr-ext-workflow), resilience and recovery (retries, backoff, timeouts, circuit breakers via tenacity), caching (Redis), and observability (OpenTelemetry traces, metrics, logs via OTLP). USE WHEN building async workers, queueing tasks, designing fault-tolerant multi-step workflows that must survive crashes, handling transient network/IO failures, instrumenting Python services for production, designing retry policies, configuring tracing/metrics, or caching with Redis. NOT FOR language idioms or type hygiene (use `writing-python`), HTTP routing (use `fastapi`), or deep OTel reference (use `otel`).