القائمة
Coordinate planner and specialist agents to decompose, retrieve, and synthesize complex RAG answers.
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
Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
| name | multi-agent-rag-orchestration |
| title | Multi-Agent RAG Orchestration |
| description | Coordinate planner and specialist agents to decompose, retrieve, and synthesize complex RAG answers. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
| category | rag-agents |
| tags | ["multi-agent","orchestration","planner","decomposition"] |
Multi-agent orchestration splits a complex question across specialized agents, such as a planner, one or more retrievers, and a synthesizer. Each agent owns a narrow job, and a coordinator sequences them and merges the results into a single grounded answer.
Some questions require several independent lookups, cross-source reasoning, or distinct skills that a single prompt handles poorly. Cramming everything into one agent produces long, unfocused prompts and answers that quietly drop parts of the question.
Have a planner break the question into subtasks that can be handled independently, with explicit dependencies where order matters.
Why: Clear decomposition prevents subtasks from overlapping or dropping parts of the original question.
Give each subtask to an agent with a single responsibility and only the tools it needs.
Why: Narrow scope keeps prompts short, decisions debuggable, and behavior predictable.
Merge partial answers in a synthesis step that resolves conflicts and preserves source citations.
Why: A dedicated synthesis step avoids contradictory fragments and keeps the final answer traceable.
For a worked pattern, see the Multi-Agent RAG example and the LangGraph multi-agent guide.
