Ingest L1

Ingest L1 - code analysis with adversarial gate

Promotes objects from L0 stub to L1 page with verified code analysis. The cycle is driven by you (main agent): you invoke sub-agents via the Task tool, the scripts handle everything else (state, validation, gate, page writing).

Architecture (see core/docs/01-pipeline-l0-l1.md and 02-adversarial-gate.md):

• author = sub-agent abap-analyzer (raw-only): reads the source and writes output/runs/<run>/<task>/author.yaml (anchored claims + dependencies).
• deepcheck = sub-agent abap-deepcheck (different model, separate session): verifies that every claim is demonstrated by the cited lines and every dependency is real. Writes deepcheck.json.
• gate fail-closed: no page is promoted without a valid, fresh verdict with full coverage.

Batch cycle

Generate run_id = run-<timestamp> and batch_id = b-<timestamp> at the start of each round.

1. Recover (always, at the start - resumes interrupted tasks without repeating work):

   python core/src/tools/pipeline.py recover
   

2. Claim author (10-15 per batch):

   python core/src/tools/pipeline.py claim --kind l1_author --limit 12 --worker <run_id>
   

   Returns JSON with the tasks. If empty, skip to step 4; if empty there too, the loop is done.
3. Fan-out author IN PARALLEL: for each task one Task(subagent_type="abap-analyzer", ...) passing in the prompt sap_name, sap_type, devclass, raw_source_path and artifact_path = output/runs/<run_id>/<task_id>/author.yaml. On return from each:

   python core/src/tools/pipeline.py submit-author --task <task_id> --run <run_id> --batch <batch_id>
   

4. Claim deepcheck and fan-out IN PARALLEL of the judge (separate session):

   python core/src/tools/pipeline.py claim --kind l1_deepcheck --limit 12 --worker <run_id>
   

   For each task Task(subagent_type="abap-deepcheck", ...) with the prompt and verdict_path as indicated. On return:

   python core/src/tools/pipeline.py submit-verdict --task <task_id> --run <run_id> --batch <batch_id>
   

5. Apply (ACCEPT only; idempotent):

   python core/src/tools/pipeline.py apply --run <run_id> --batch <batch_id>
   

6. Project + commit:

   python core/src/tools/pipeline.py project
   python core/src/tools/pipeline.py export-excel
   python core/src/tools/pipeline.py git-commit --message "ingest L1 batch <batch_id>" --batch <batch_id>
   

7. Progress and decide whether to continue:

   python core/src/tools/pipeline.py progress --json
   

Autonomous execution with /loop

Prerequisite: Claude Code session open with working directory = repo root, so the sub-agents abap-analyzer and abap-deepcheck (in .claude/agents/) are registered and invocable via the Task tool. Verify with sync_agents.py --check.

Command (example for a single package; omit --package for all):

/loop Continue the L1 ingest of package ZPACKAGE until complete.
Each iteration, from the repo root with the venv (.venv\Scripts\python):
1) python core/src/tools/pipeline.py recover
2) Generate RUN=run-<timestamp> and BATCH=b-<timestamp>.
3) python core/src/tools/pipeline.py claim --kind l1_author --limit 10 --worker $RUN --run $RUN --batch $BATCH --package ZPACKAGE
   If it returns [], skip to step 6.
4) For EACH task in the claim, IN PARALLEL: Task(subagent_type="abap-analyzer") passing in the prompt
   sap_name, sap_type, devclass, raw_source_path and artifact_path=output/runs/$RUN/<task_id>/author.yaml.
   On return from each: python core/src/tools/pipeline.py submit-author --task <task_id> --run $RUN --batch $BATCH
   (if the agent fails: submit-author --task <id> --run $RUN --batch $BATCH --fail "msg").
5) python core/src/tools/pipeline.py claim --kind l1_deepcheck --limit 10 --worker $RUN --run $RUN --batch $BATCH --package ZPACKAGE
   For EACH task IN PARALLEL: Task(subagent_type="abap-deepcheck") passing prompt_path=output/runs/$RUN/<author_task_id>/deepcheck-prompt.txt
   and verdict_path=output/runs/$RUN/<deepcheck_task_id>/deepcheck.json (author_task_id is the latest l1_author task for the object).
   On return: python core/src/tools/pipeline.py submit-verdict --task <deepcheck_task_id> --run $RUN --batch $BATCH
6) python core/src/tools/pipeline.py apply --run $RUN --batch $BATCH
7) python core/src/tools/pipeline.py project
8) python core/src/tools/pipeline.py git-commit --message "ingest L1 ZPACKAGE batch $BATCH" --batch $BATCH
9) python core/src/tools/pipeline.py progress --package ZPACKAGE --json
   If "l1_remaining" is 0, STOP the loop. Otherwise continue with the next iteration.
Objects with REVERT status return to the queue automatically and are retried in subsequent rounds (max 3 attempts, then failed).

Note: if you invoke abap-deepcheck via the Task tool, use the registered subagent_type (it already has model: sonnet in the frontmatter → automatic model independence). To map deepcheck_task to the object's author_task, use the latest l1_author for the same object_id (one per object).

Rules

• The author and the deepcheck MUST NEVER run in the same session/context: judge independence is the anti-hallucination guarantee.
• Per-task errors do not block the batch: the failed object stays in the queue (up to max-attempts) or goes to failed; the loop continues.
• A rejected submit-author (schema/hygiene KO) consumes one attempt AND returns the object to l1_ready (it leaves authoring). Before re-running submit-author on a corrected artifact you MUST re-claim --kind l1_author that object first, otherwise it errors with InvalidTransition: 'l1_ready' -> 'authored' not allowed. Re-claiming reuses the same task id (and the same output/runs/<run>/<task_id>/author.yaml path), so a fixed artifact is picked up as-is.
• Gate outcomes: ACCEPT promotes; REVERT sends back to author with findings (max 3 attempts, then failed/escalation); BLOCKED re-runs only the deepcheck.
• Discovered SAP standards must be resolved with the separate MCP loop (claim --kind mcp_lookup), when the abap-fs server is running. See core/docs/05-runbook.md.