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name: research-l2 description: "Executes Phases 1-3 of the L2 process of the abap_wiki knowledge base on a slice: launches the abap-functional-researcher sub-agent (gap discovery + multi-source auto-research: wiki -> raw/docs -> MCP abap-fs on the <SAP_DEV_SYSTEM> system read-only -> standard knowledge), ingests gaps and evidence, and generates questionnaires for experts from the residual load-bearing gaps. Use this skill after slice-init to functionally document a process. Prerequisite: a slice with a real owner."

Research L2 - gap discovery, auto-research, questionnaires

Promotes knowledge from what the code does (L1) to why it exists and which process it serves (L2), per slice. The cycle is driven by you (main agent): you invoke the sub-agent abap-functional-researcher, the scripts handle the rest (persistence, views, questionnaires).

Architecture (see core/docs/03-l2-process.md):

• researcher = sub-agent abap-functional-researcher: reads the L1 pages of the rich_target + the graph, discovers functional gaps, attempts to answer them autonomously (wiki -> raw/docs -> MCP abap-fs <SAP_DEV_SYSTEM> read-only -> standard [INFERRED]), writes citable evidence files and the research.yaml artifact.
• submit-research persists gaps+evidence and closes auto-answered gaps as [VERIFIED].
• questionnaire triages the residual load-bearing gaps by recipient, with the pre-filled hypothesis: the expert confirms/corrects instead of writing from scratch.

Prerequisite: slice-init already run (the slice has membership + real owner). The MCP abap-fs server running reduces the number of human questions; if it is off, auto-research proceeds on wiki+standards and nearly all gaps become questionnaire items (still useful).

Cycle

1. Get the rich_target (the objects to document, with their L1 page paths):

   .venv/Scripts/python core/src/tools/pipeline.py slice-targets --slice <slice-id>
   

   Returns JSON [{slug, sap_type, sap_name, devclass, page_path, hop, role}, ...].

2. Fan-out of the researcher (one per slice, or in batches if the rich_target is large - e.g. 8-10 objects per invocation). For each batch, one Task(subagent_type="abap-functional-researcher", ...) passing in the prompt:

   • slice_id, owner (from the manifest),
   • rich_target = the subset of objects in the batch (slug + page_path),
   • membership_path = slices/<slice-id>/membership.md,
   • research_artifact_path = output/l2/<slice-id>/<batch>/research.yaml. The agent writes the evidence files under slices/<slice-id>/research/ and the artifact.

3. Ingest each artifact (gap numbering is progressive per slice, submits accumulate):

   .venv/Scripts/python core/src/tools/pipeline.py submit-research --slice <slice-id> --file output/l2/<slice-id>/<batch>/research.yaml
   

4. Generate questionnaires for the residual load-bearing gaps (status open). Single owner answering everything -> one questionnaire:

   .venv/Scripts/python core/src/tools/pipeline.py questionnaire --slice <slice-id> --dest all
   

   Or triaged by role (one per recipient):

   pipeline.py questionnaire --slice <slice-id> --dest business
   pipeline.py questionnaire --slice <slice-id> --dest developer
   pipeline.py questionnaire --slice <slice-id> --dest customizing
   

   Files go to slices/<slice-id>/interviews/<date>-<slice>-<dest>.md with the pre-filled hypothesis and an Expert answer block to be filled in.

5. Status + commit (manual, the user commits):

   .venv/Scripts/python core/src/tools/pipeline.py l2-progress --slice <slice-id>
   git add slices/<slice-id> state/ && git commit -m "research L2 <slice-id>"
   

When the owner has filled in the questionnaires, use the capture-answer skill to ingest them; the answers become canonical evidence and the gaps move to answered.

MCP usage (researcher, read-only)

• execute_data_query on TBTCO/TBTCP (by program name) -> direct proof of TRIGGERS (job, frequency, variant);
• get_abap_object_info -> long text of transactions/data elements/domains (FIELD-SEMANTICS);
• find_where_used -> ACTOR/INTEGRATION;
• COUNT(*)/SELECT DISTINCT on Z tables -> DATA-LIFECYCLE / field values. Each MCP result becomes a dated evidence file (source: mcp, system: <SAP_DEV_SYSTEM>).

Rules

• The researcher is READ-ONLY: it does not modify SAP objects, does not write code, does not touch raw/.
• Every hypothesis/answer carries a confidence tag; a load-bearing gap closes on its own only if [VERIFIED] (mcp/wiki/raw-docs). Others remain for the questionnaire.
• gaps.yaml is a regenerated view: do not edit it manually. State lives in the DB.
• No auto-commit in the skill: committing the slice is the user's responsibility.