ai-agent-operations

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}

AI Agent & RAG System Operations

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.

When to Use

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.

Prerequisites

• Conda environment initialized.
• Qdrant Vector database or OpenAI API credentials set in environmental variables.

Process

Follow these procedures to query RAG models and index agent knowledge.

Executing RAG Search via CLI

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?"

Rebuilding Library TOCs

Regenerate document tables of contents for optimal chunk indexing:

conda run -p D:\Anaconda\envs\cursor-factory python scripts/ai/rag/rebuild_tocs.py

Best Practices

• Optimize Prompt Tokens: Leverage the memoization layer to cache repetitive API requests.
• Library Audits: Routinely run repair_library.py to fix missing document metadata.