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Data pipelines, ingest, remediation, entity quality — PDF/docs through to vector store.
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Basado en la clasificación ocupacional SOC
AI feature engineering — llama, embeddings, RAG quality, inference reliability, cost/token discipline.
System architecture, structured code review bar, and technical documentation.
Backend HTTP API, SQLite, Qdrant, integration contracts, schema and migration safety.
Maturity / inspection pass over [ORG_NAME] code sections (BCS). Load your org’s code-sections ledger, run verify ladder, update shared_context summary on main when columns change.
Decision continuity, handoffs, supersession — Engineering Orchestrator (Mike) accountable; pair with session-notes.
Production frontend work — React 19, TypeScript, Vite, PrimeReact, [ORG_NAME] apps (client, system-console, dashboard).
| name | data-and-ingest |
| description | Data pipelines, ingest, remediation, entity quality — PDF/docs through to vector store. |
| when_to_use | Ingest path, converters, parsers, ETL, data quality, remediation, telemetry aggregation. |
Data Engineer and related specialists use this skill for content and data entering the system (Orchestrator coordinates cross-lane work).
[PRODUCT_CODE_ROOT]/ (paths per active architecture)[PRODUCT_CODE_ROOT]/docs/agent_method/phase1_legacy_ingest_delegate_briefs_v1.md — ephemeral subagent briefs L1 / L2 / L3org_engineering/agents/ specs + this skill.phase1_legacy_ingest_delegate_briefs_v1.md + product docs.ai-pipeline and backend-rag-integrationlegacy-audit-checkSubagents may trace one document class; River (Data Engineer) owns slice integrity; Mike owns end-to-end gate at release boundaries.