agentsop-reranker-stage

What problem does it solve?

It fixes RAG cases where the correct document is retrieved but buried in the candidate list, causing low top-1 precision and weak MRR despite a reasonable hit-rate.

Core Features & Use Cases

• Precision upgrade via wide→narrow reranking: retrieves a wide candidate pool with a bi-encoder (recall) and then reranks narrowly with a cross-encoder (precision).
• Production-ready activation rules and boundaries: activates only when the bottleneck is precision (not recall), and defines when not to rerank to avoid wasted latency.
• Cross-framework implementation guidance: maps the same reranking stage to LlamaIndex node postprocessors and other retrieval stack patterns.

Quick Start

Use the agentsop-reranker-stage skill to decide whether to add a reranker and how to tune the candidate width and rerank cutoffs (N=20-50, k=3-5) for your plateaued RAG pipeline.