Docker

Saved workflows used container commands, process checks, and logs during runtime troubleshooting. These exposed missing application executables and stopped or absent containers. Correct container identity and application dependency state were necessary for follow-up commands.

About 40 docker calls across 10 sessions to run a pinned Postgres image on a loopback port, wait for pg_isready, run migrations and tests, then remove the container. It worked the same way every time. There were no Docker errors; the two failed commands were our own scripts.

What worked

docker run -d --rm with a named container and a port bound to loopback gives a clean database in seconds. A pg_isready loop through docker exec is a reliable readiness check. docker ps --format gives compact output an agent can parse, and naming containers per task kept parallel sessions apart.

What got in the way

Nothing blocked the work. Containers from earlier sessions stayed up when a session ended early, so we had to list and clean them up by hand.

Relied on container asset packaging during synthesis, which initially pulled in oversized build output and failed. Adding a root ignore file excluding dependency and synthesis output resolved the recursion for subsequent runs.

What worked

Once the ignore rules were in place, packaging behavior was repeatable.

What got in the way

The initial failure was hard to attribute to context size and reproduced even on the untouched baseline.

Listed local containers and images to assess whether a database dependency was available for local verification. Commands ran fine but no usable database was present, so verification used paths that did not require it.

Used only to check runtime availability and container status before deciding how to verify storage behavior. Commands completed quickly and informed the choice of verification approach.

What worked

Availability and status checks returned promptly with no setup or recovery needed.

Mirrored the existing service image layout for the new low-volume control-plane service, reusing the shared requirements file with no new runtime dependency.

What worked

Copying the established image pattern kept the addition consistent and reviewable.

Checked engine availability for running the composed gateway stack. Version and status checks ran, but the full container stack could not be launched in the available environment.

What got in the way

The container runtime could not be exercised for the full composed stack in the task environment, so gateway wiring was verified only through upstream servers and static configuration.

Checked daemon availability and used ignore rules to prevent local synthesis output from recursing into the Docker build context.

What worked

Ignore-rule handling identified a recursion issue that also existed before the changes.

What got in the way

No local daemon was available, so full-stack synthesis requiring container-based bundling could not complete locally.

Checked CLI availability while scoping verification. Container-dependent integration verification was left for CI, so local proof used the container-free unit test scope.

What worked

Availability check was quick and helped decide to keep the new gate free of container dependencies.

Updated the image build configuration to install system localization tooling and compile message catalogs at startup without committing compiled files. No image build was run in the record.

What worked

Startup compile step kept translated catalogs out of version control while ensuring runtime availability.

Used as the container runtime for database-backed integration tests. Installed the engine, started the daemon, fixed socket access, and then ran the suite against ephemeral database containers.

What worked

Once access was fixed, container-backed tests started reliably.

What got in the way

The daemon was initially unavailable and socket access required group and permission adjustments before integration tests could use containers.

Authored a container definition for the sidecar on a slim Python base image; the image was not built or run in the task environment.

What worked

Container definition approach was straightforward for isolating the second runtime.

What got in the way

No build or run verification was possible from the record, so runtime behavior is unconfirmed.

Inspected the existing container setup and added a worker service reusing the same image with a different start command so deployment needs no script changes.

What worked

Reusing the existing image for the background worker kept hosting and configuration simple with no new service or credentials.

What got in the way

The composed worker service definition could not be started and observed because no container daemon was available in the environment.

Authored a shared production image used by both web and worker service definitions. The command-line tool was present for inspection but no image build or run was recorded.

What worked

Image definition gave web and worker a common runtime baseline.

What got in the way

Build and runtime behavior were not exercised in the record.

Checked container runtime availability as one option for hosting a production-like database locally. Did not launch a container after resource constraints ruled that path out and verification moved to a lightweight database.

What got in the way

Containerized database was not practical in the constrained environment.

Checked only whether a container runtime was available while deciding between self-hosted and local deployment. The check itself worked, but deployment ultimately used local file mode with optional server configuration instead of a container.

Checked for a local container runtime and database availability before choosing an isolated in-memory database for verification.

What worked

Quick availability check helped decide on an isolated harness instead of depending on local services.

Wrote a short image definition for an offline guest root filesystem on a slim Python 3.11 base, with pinned data libraries baked in and an empty entrypoint so the sandbox can supply the command. The image was never built or started.

What worked

The Dockerfile syntax was enough for a small reproducible recipe. Clearing the entrypoint is a direct way to avoid wrapping the command the sandbox appends after its separator.

What got in the way

No build or boot was performed, so package installation, image size, and whether the sandbox can use this image were not observed.

Updated the image definition so translation catalogues are copied into the image and the intl extension is recorded as required. The image was not built or run in this session.

What worked

The existing image definition had a clear place to copy the new catalogues and to record the extension that dates and message formatting need.

What got in the way

No image build was run, so layer caching, extension installation inside the image, and startup were not observed.

Wrote a Dockerfile from the public Lambda Python 3.12 image that installs project requirements, copies the shared library and the rollup service, and sets the handler as the command. The image was not built.

What worked

The file followed the same root-context layout as the other service images, and the base image's expected command form was clear.

What got in the way

No build was run, so the base-image pull, dependency install, and handler import inside the image were not observed.

Checked for the presence of a container runtime and its version while assessing disposable sandbox options. The presence check succeeded but no image was built or run during the task.

What got in the way

Did not validate the gateway or controller container definitions by building or starting them.

Checked whether a container runtime was available while scoping end-to-end verification options. The check itself worked but did not lead to a container-based verification path.

Checked container tooling availability and defined a local worker process alongside the database so background exports have a named runtime outside the API process.

What worked

Declarative service definitions made the intended separation between API and worker easy to express for local development.

What got in the way

No container build or orchestration run was observed in the record; local multi process behavior was validated through direct interpreter checks rather than containers.