Docker Expert-AI-powered Docker tool for automation.

Example

Turn a naive development image into a secure, small, cacheable production image. Example tasks include converting a single-stage Node/Go/Java Dockerfile into a multi-stage build, splitting dependency installation from source copy to leverage build cache, selecting minimal base images (alpine, distroless) when appropriate, and removing build artifacts before final image.

Scenario

A Node.js API initially ships a 900MB image because it includes dev tools and build caches. Docker Expert rewrites the Dockerfile into a two-stage build: first stage installs and builds (node:20-alpine), second stage copies only runtime artifacts into a slim base (node:20-alpine or gcr.io/distroless/nodejs). Result: image size reduced to ~60–120MB, faster pulls, and lower cold-start times. Commands provided: `docker build --tag company/api:1.2.0 .` and `docker run --rm -p 3000:3000 company/api:1.2.0 --env NODE_ENV=production`.

Example

Compose setups that match production service topology but are optimized for developer velocity: using bind mounts for code, healthchecks for test orchestration, dependency wait scripts, and separate compose override files for dev (`docker-compose.override.yml`) to enable live reload while keeping `docker-compose.yml` clean for CI/production.

Scenario

A full-stack developer needs to run frontend, backend, and Postgres locally with realistic data. Docker Expert supplies a `docker-compose.yml` with service definitions and a `docker-compose.dev.yml` that adds `volumes: - ./src:/app`, `environment: - NODE_ENV=development`, and a lightweight seed job. Developer runs: `docker compose -f docker-compose.yml -f docker-compose.dev.yml up --build`, enabling quick iterative changes and deterministic integration tests that mirror the CI environment.

Example

Define a CI pipeline (GitHub Actions, GitLab CI, Jenkins) that: checks out code, builds an image with buildkit, runs unit and integration tests inside containers, performs vulnerability scanning (Trivy/Clair), produces an SBOM, signs the image (cosign), and pushes to the registry with immutable semver tags + `latest` for non-prod branches. Also include automated promotions: e.g., from `staging` tag to `production` after smoke tests and manual approval.

Scenario

On merge to `main`, Docker Expert provides a GitHub Actions job: `build` (uses cache-from to speed builds), `test` (runs integration tests against container), `scan` (Trivy scan fails pipeline if critical CVEs found), and `publish` (push to ECR with `company/api:vX.Y.Z` and generate SBOM). A separate `promote` pipeline takes a validated image tag and deploys to Kubernetes via ArgoCD/Helm. This reduces human error, ensures images are scanned before production, and makes rollbacks a simple image tag switch.