opensearch-function-scoring-algorithms

opensearch-function-scoring-algorithms is a RAG & retrieval skill for AI agents, published by pproenca in the dot-skills repository on GitHub. Skillspace rates opensearch-function-scoring-algorithms C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

180

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

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