Author search-engine relevance regression tests for Elasticsearch, OpenSearch, and Apache Solr. Core workflow on the Elasticsearch Ranking Evaluation API (`POST {index}/_rank_eval`) - judgment lists (query + expected docs at ranks), per-query metrics (Precision@K, Recall@K, MRR, DCG, ERR), reproducible test corpora; pair with Quepid + Splainer for interactive judgment authoring. Per-engine references cover the OpenSearch delta (Search Relevance Workbench, neural query DSL, hybrid BM25 + neural pipelines, ES-to-OS migration parity) and the Apache Solr delta (no _rank_eval: debugQuery score explain, LTR feature/model store REST, eDisMax qf/pf/mm tuning, external nDCG harness). Use before changing analyzers, synonyms, boosts, or query templates on an Elasticsearch, OpenSearch, or Solr index that serves user-facing search, so the NDCG / MRR baseline is captured first.
71
89%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
Passed
No findings from the security scan
Tessl evals compare success rates of agents with and without our optimized context