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AI model configuration, LLM memoization, agentic RAG search, and vector library maintenance.
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Baseado na classificação ocupacional SOC
Automated governance, hook installation, pre-commit validation, branch isolation, and safe commit operations.
Enforcement of safety guardrails, axiom verification, secret scanning, and mutability protections.
Building and managing premium statistical dashboards, RAG explorer UIs, warehouse log monitors, and real-time visualization centers.
Integration of agent systems with blockchain protocols, contracts, event stores, reputation networks, and collective verification engines.
Catalog generation, reference link verification, workshop documentation building, and knowledge gap analysis.
Master system orchestration, blueprint rendering, project scaffolding, multi-agent debates, and SDLC stage validators.
| name | ai-agent-operations |
| description | AI model configuration, LLM memoization, agentic RAG search, and vector library maintenance. |
| type | skill |
| version | 1.0.0 |
| category | routing |
| agents | ["python-ai-specialist","master-system-orchestrator"] |
| knowledge | ["ai-integration-patterns.json"] |
| scripts | ["scripts/ai/agents/rag_agent.py","scripts/ai/core/llm_config.py","scripts/ai/core/memoization.py","scripts/ai/memory/ground_ideation.py","scripts/ai/rag/agentic_rag.py","scripts/ai/rag/rag_cli.py","scripts/ai/rag/rebuild_library.py","scripts/ai/rag/rebuild_tocs.py","scripts/ai/rag/repair_library.py"] |
| tools | [] |
| related_skills | ["applying-rag-patterns"] |
| references | [] |
| settings | {"auto_approve":false,"timeout_seconds":300} |
This skill covers the configuration and operational execution of Large Language Models (LLM), response memoization layers, agentic RAG searches, and vector database document library maintenance.
Use this skill when configuring LLM endpoints, setting up response memoization caches, running CLI RAG searches, rebuilding library Tables of Contents (TOC), or repairing vector indices.
Follow these procedures to query RAG models and index agent knowledge.
Query the vector store directly:
conda run -p D:\Anaconda\envs\cursor-factory python scripts/ai/rag/rag_cli.py --query "What is the 5-layer architecture?"
Regenerate document tables of contents for optimal chunk indexing:
conda run -p D:\Anaconda\envs\cursor-factory python scripts/ai/rag/rebuild_tocs.py
repair_library.py to fix missing document metadata.