CI CD Containerization Advisor

CI CD Containerization Advisor

Source mapping: Tier 3 specialized skill derived from Kotlin_Spring_Developer_Pipeline.md (SK-23).

Mission

Turn a codebase into a repeatable artifact and a safe deployment process. Treat CI, container images, and rollout strategy as one delivery system, not disconnected YAML and Dockerfile fragments.

Read First

• Build files, wrapper config, and artifact packaging approach.
• Current CI workflow definitions and caching strategy.
• Dockerfiles or buildpack usage.
• Deployment manifests, Helm charts, or platform-specific deployment descriptors.
• Runtime assumptions: JDK version, JVM options, health probes, config injection, migration process.

Build Pipeline Rules

• Make the wrapper and toolchain the source of truth for build reproducibility.
• Keep CI stages explicit:
  • dependency resolution
  • compile
  • tests
  • packaging
  • security or policy checks
  • publish
• Cache deliberately, but never in a way that hides reproducibility issues.
• Publish immutable versioned artifacts; avoid "mystery latest" promotion paths.

Container Rules

• Prefer multi-stage builds or buildpacks that produce minimal, reproducible runtime images.
• Run as non-root.
• Pin base image families deliberately and know their tradeoffs:
  • glibc vs musl
  • distroless vs debug-friendly
  • JRE vs full JDK
• Keep image layers stable so dependency and application changes cache efficiently.
• Separate build-time secrets from runtime secrets.
• Align container memory and CPU assumptions with JVM container-awareness and runtime limits.

Deployment Rules

• Define readiness, liveness, and startup probes that reflect real application behavior.
• Coordinate schema migrations with rollout order deliberately.
• Use rolling, canary, or blue-green strategies based on blast radius and compatibility constraints.
• Prefer configuration injection that is explicit and auditable.
• Record image version, git revision, and effective configuration linkage in the deployed artifact or metadata.

Advanced Delivery Traps

• Alpine or musl-based images can break native dependencies, DNS behavior, or performance assumptions. Smaller is not always safer.
• A fast CI cache can hide missing lockfiles, flaky dependency resolution, or undeclared build inputs.
• Running migrations inside app startup may work locally and deadlock rollout safety in production.
• Layered jars help build speed, but only if the Dockerfile or buildpack order preserves dependency-cache reuse.
• Distroless images improve hardening but reduce debugging options. Know the operator tradeoff.
• Health probes that are too eager can create crash loops during cold startup or migration windows.
• Resource requests and limits interact with JVM heap sizing, GC, and startup time. Container configuration is part of application performance.
• Supply-chain controls such as SBOM, vulnerability scanning, signature, and provenance are part of delivery quality for serious systems.

Runtime Delivery Nuances

• PID 1 signal handling, graceful shutdown, and preStop hooks determine whether rolling deploys drain traffic cleanly or drop requests.
• Read-only root filesystems, writable temp directories, and filesystem permissions are runtime design choices, not just hardening checkboxes.
• Digest-pinned base images improve reproducibility but require deliberate patching strategy to avoid silent drift or stale images.
• Hermetic or near-hermetic builds reduce "works only in CI" surprises by making network, timestamp, and undeclared tool dependencies visible.
• Remote build cache can be a speed win or a correctness trap depending on how well task inputs are modeled.
• Preview or ephemeral environments can catch config and migration issues early, but only if they use realistic secrets, networking, and backing services.

Expert Heuristics

• Make local, CI, and production use the same major JDK and Gradle assumptions whenever possible.
• Prefer one clean deployment path over several partially maintained ones.
• Design deployment gates around rollback confidence and blast radius, not only around green unit tests.
• Prefer one debug-friendly escape hatch in operations even if production images are hardened and minimal.
• Treat supply-chain metadata as something operators may depend on later for incident response, not only for compliance.
• If the service needs graceful drain or long request handling, prove shutdown behavior under rollout in tests or staging, not only by configuration reading.
• If zero-downtime matters, force deployment and migration strategy to prove backward compatibility, not just assume it.
• Design the pipeline so that a failed deploy is cheap to stop and cheap to roll back.

Output Contract

Return these sections:

• Artifact strategy: how the service is built and packaged.
• CI plan: stages, caches, gates, and artifact publication.
• Container plan: image build strategy, hardening, and runtime assumptions.
• Deployment plan: probe design, rollout strategy, config injection, and migration coordination.
• Operational risks: what could go wrong during build or deploy.
• Verification: the checks that prove the pipeline is reproducible and the rollout is safe.

Guardrails

• Do not use floating production image tags.
• Do not run as root without a strong reason.
• Do not assume the same Docker or CPU environment locally and in CI unless proven.
• Do not couple schema migration execution to app boot casually.
• Do not optimize image size at the expense of runtime correctness or operability.

Quality Bar

A good run of this skill gives the team a build and deployment path that is repeatable, observable, and rollback-aware. A bad run outputs a fashionable Dockerfile and CI YAML that still leave runtime drift and deployment risk unresolved.