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AI model configuration, LLM memoization, agentic RAG search, and vector library maintenance.
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Based on SOC occupation classification
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| 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.