Brings up the documented target architecture as a docker-compose stack — a
modular monolith with the Ghidra step split into its own async worker.
- worker/: RQ queue (lazy redis import) + run_acquisition task (Job status
queued→started→finished/failed, drives ams.acquire with sink=db)
- Job model + JobOut schema; Snapshot.data is JSONB on Postgres
- POST/GET /jobs: stream an upload to a shared volume, enqueue, poll status
- docker/api.Dockerfile (slim) + docker/worker.Dockerfile (JDK21 + Ghidra
fetched at build, overridable via GHIDRA_URL) + docker-compose.yml
- ghidra.py: AMS_GHIDRA_SCRIPTS override for in-container script path
- pyproject: [worker] extra (rq/redis/psycopg), python-multipart in [api]
- tests: 4 new (task success/failure + endpoint enqueue/503) -> 22/22
Verified: API image builds, container serves /health + /ui + /jobs; compose
config validates. Worker image (downloads ~1 GB Ghidra) not built here.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Closes the chain from a game file to a catalog entry: unpack an ISO/ZIP,
content-identify the engine DLL (CMC_ObjectsContainer marker in RTTI, so a
renamed file is still found), hash it (sha256 + md5 + optional ssdeep via
ppdeep), run Ghidra headless with the extractor, enrich and import the snapshot.
- unpack.py: bsdtar (ISO9660 + ZIP) with a pure-Python zipfile fallback
- identify.py: content-based engine-DLL picker + hashing
- ghidra.py: analyzeHeadless launcher discovery + post-script run
- pipeline.py: orchestration with injectable extract_fn; sink db|http|none
- cli.py: python -m ams.acquire (incl. --identify-only dry run)
- tests: 7 new (forged PE markers + stubbed extractor) -> 18/18
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
