Rank retrieved documents by similarity_score or distance with thresholds and top-k selection.
npx skills add https://github.com/edangx100/adaptive_rag_vercel --skill ranking
Please help me install this Agent Skill. Skill: ranking Source: https://github.com/edangx100/adaptive_rag_vercel/tree/main/.claude/skills/ranking Command: npx skills add https://github.com/edangx100/adaptive_rag_vercel --skill ranking
This Skill eliminates the manual sorting of search results by automatically ranking documents based on similarity metrics.
Use the ranking skill to sort the 15 retrieved documents about gaming laptops by their relevance scores.
High-intent search queries and answers about installing and using this skill.
Relevance sorting ranks retrieved documents by their similarity scores in descending order, prioritizing the most relevant content first. This eliminates manual review and saves time by surfacing top matches immediately.
Ranking operates on precomputed similarity metrics from retrieval—specifically similarity_score and distance values. You can sort by similarity_score descending for relevance or distance ascending for proximity.
Yes, ranking applies a similarity threshold to filter documents, then selects top-k results across collections. This ensures only sufficiently relevant documents pass to downstream grading or generation tasks.
No, ranking operates strictly on precomputed metrics without triggering new data-store queries. It processes metrics already attached to retrieved documents for deterministic, collection-aware output.
Ranked results flow to downstream grading or generation tasks. The ordered list preserves relevance context for review prioritization or content synthesis workflows.
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