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Hybrid-RAG-example
Hybrid-RAG-example에는 FullFran에서 수집한 skills 18개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
Help the team document and maintain Architecture Decision Records (ADRs).
Expert guidance on document processing with Docling and audio transcription with Whisper.
Standard for creating technical documentation in this repository. Use this when writing new documentation in docs/ to ensure consistent hierarchy and formatting.
Expert guidance on creating accurate, visually polished Mermaid diagrams for architecture documentation.
Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
Expert guidance on building agents and tools with Pydantic AI.
Best practices for async Python code, avoiding common pitfalls like await precedence bugs and sync-in-async anti-patterns.
Create and initialize new Agent Skills following the agentskills.io standard. Use this when you need to modularize a new capability for the AI agent.
Expert guidance on Supabase/PostgreSQL implementation for RAG, including pgvector semantic search and full-text search.
Create new Antigravity workflows to automate repetitive tasks. Use this when the user wants to formalize a multi-step process into an automated workflow.
Apply AI orchestration patterns (routing, tool use, ReAct, fallbacks) to design reliable agent flows.
Apply Clean Architecture boundaries, dependency rules, and layer responsibilities when designing or changing code in this repository.
Guide architecture decisions, document tradeoffs, and align designs with system goals and constraints.
Best practices for async Python code, avoiding common pitfalls like await precedence bugs and sync-in-async anti-patterns.
Expert guidance on document processing with Docling and audio transcription with Whisper.
Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
Expert guidance on building agents and tools with Pydantic AI.
Expert guidance on Supabase/PostgreSQL implementation for RAG, including pgvector semantic search and full-text search.