# Full-release 96-cell execution plan ## Objective Publish and verify one Docker image for every frozen task-tier cell in `manifests/task_matrix.csv`: 48 tasks times `lite` and `standard`, for 96 images total. The Lite release is the packaging reference: downloadable Docker archives are grouped by track. The Full release preserves its stricter isolation and data policy: task images contain code, prompts, contracts, and provenance only. Dataset bytes are mounted at runtime and are never embedded in an uploaded image. ## Artifact layout - Build one immutable task image per matrix row, tagged under the `automedbench-full/` namespace and locked to one task and one tier. - Retain the common agent runtime and seven track evaluator runtimes. - Group exports into the same seven per-track paths and archive names used by AutoMedBench-Lite. Each archive contains all task-tier images for its track; the largest contains 28 images. The already-published runtime archive supplies the shared agent and seven evaluator images. - Process upload archives serially. Keep each local archive until the private Hugging Face remote reports the same size and SHA-256, then remove only that verified archive and its uniquely namespaced task tags. - Never run a global Docker prune or remove unrelated images, containers, volumes, or caches. ## Dataset resolution For every task, record one of these outcomes with source revision, license, selected IDs, layout checksum, and evidence: 1. `staged_verified`: authorized source data were deterministically staged and scanned. 2. `acquisition_ready`: the source and staging recipe are frozen, but an authorized user must supply gated data. 3. `externally_blocked`: access approval, a DUA, or upstream clarification is required and cannot be substituted by a private upload. Restricted or unclear-license data may be used locally only when access is already authorized. They must not be uploaded. Synthetic inputs may verify a container contract, but never change a task's dataset readiness status. ## Verification gates Each of the 96 cells must pass: 1. task schema, internal checksums, path safety, and credential scan; 2. Docker build with a digest-pinned base and immutable task/tier labels; 3. non-root container self-test and task/tier identity check; 4. prompt and output-contract rendering against a staged or synthetic public item; 5. a bounded ChatGPT API workflow smoke run covering S1 and S2 only; 6. termination before S3 validation starts; 7. exported-archive OCI graph, layer digest, tag inventory, and secret scan; 8. private remote size and SHA-256 equality after upload. S1-S2 smoke results are launch/readiness evidence only. They are not benchmark scores and must not be presented as task completion. ## Execution order 1. Audit local/remote access, disk, Docker, GPU visibility, and credentials without printing sensitive values. 2. Resolve public datasets first; freeze acquisition recipes for gated tasks; update readiness records from evidence. 3. Implement and test the task-image builder, S1-S2 verification mode, archive exporter, and remote verifier. 4. Build and verify all 96 images. 5. Run S1-S2 smoke verification in bounded batches, stopping before S3. 6. Export and upload one track at a time, verifying the remote before cleanup. 7. Audit the final 96-image inventory and update release documentation. ## Known environmental constraints at plan freeze - LLM verification uses the configured remote ChatGPT-compatible API only; host GPUs are intentionally not used. - Docker is available through passwordless `sudo`; the user is not in the `docker` group. - The Docker daemon does not currently have NVIDIA Container Toolkit support. This is not a release blocker because the requested S1-S2 verification is CPU-safe and uses the remote API; verification launches omit `--gpus`. - The configured ChatGPT-compatible endpoint and private Hugging Face token both authenticate successfully. - Gated/DUA datasets remain external dependencies even when the build and upload pipeline is otherwise complete.