rag-reranking
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
npx skills add a5c-ai/babysitter --skill rag-reranking --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RAG Reranking Skill ## Capabilities - Implement cross-encoder reranking models - Configure Maximal Marginal Relevance (MMR) filtering - Set up Cohere Rerank integration - Design multi-stage retrieval pipelines - Implement diversity-aware reranking - Configure score normalization and thresholds ## Target Processes - advanced-rag-patterns - rag-pipeline-implementation ## Implementation Details ### Reranking Methods 1. **Cross-Encoder Reranking**: Sentence-transformer cross-encoders 2. **Cohere Rerank**: Cohere rerank-v3 API 3. **MMR Reranking**: Diversity-aware result filtering 4. **LLM Reranking**: Using LLM for relevance scoring 5. **Reciprocal Rank Fusion**: Combining multiple retrievers ### Configuration Options - Reranking model selection - Top-k after reranking - MMR lambda (relevance vs diversity) - Score threshold filtering - Batch size for reranking ### Best Practices - Use cross-encoders for quality - Balance relevance and diversity - Set appropriate thresholds - Monitor reranking latency ### Dependencies - sentence-transformers - cohere (optional)
- Capabilities
- Target Processes
- Implementation Details
- Reranking Methods
- Configuration Options
- Best Practices
- Dependencies
What does the rag-reranking skill do?
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill rag-reranking --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.