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AI feature engineering — llama, embeddings, RAG quality, inference reliability, cost/token discipline.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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.
Data pipelines, ingest, remediation, entity quality — PDF/docs through to vector store.
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 | ai-pipeline |
| description | AI feature engineering — llama, embeddings, RAG quality, inference reliability, cost/token discipline. |
| when_to_use | Model wiring, prompt boundaries, retrieval quality, embedding dims, inference failures, routing. |
AI Engineer Lead uses this skill when changing how the product thinks (models, RAG, prompts), not just UI.
[ENGINEERING_REPO]/CLAUDE.md / agents/core/engineering-orchestrator.md (version state)engineering-ai-engineer-lead.md + ai-pipeline instead of _archive/ paths.security-compliance-evidence + HQ claims matrixSubagents may probe prompts/tests; Mike integrates and validates against evidence.