القائمة
AI feature engineering — llama, embeddings, RAG quality, inference reliability, cost/token discipline.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| 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.
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).