القائمة
Complete flow from index creation to working vector search with Elasticsearch
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Debug and analyze LLM eval runs — view traces, compare runs, investigate failures, track costs. Use when debugging @kbn/evals failures, comparing eval runs, or analyzing LLM performance.
Start your security session with a personalized briefing — attacks, alerts, cases, rules, threat intel. Use as the first thing when starting security work.
Guide users from zero to a working Elastic cluster — Cloud or on-prem, connection config, first queries, and next steps.
Interactive guide for creating an APM service overview dashboard — discovers service data, presents metrics, and creates a tailored dashboard.
Interactive guide for creating SLOs from discovered APM and metric data — identifies candidates, lets user configure targets, and creates SLOs.
Create, configure, and manage Elasticsearch indices — mappings, settings, templates, data streams, and lifecycle policies.
| name | vector-search-setup |
| description | Complete flow from index creation to working vector search with Elasticsearch |
Use this skill when the user wants to add vector/semantic search to their application.
create_index with a mapping that includes a dense_vector field (dimensions match your model, e.g. 384 or 768).create_inference_endpoint for an embedding model (e.g. ELSER or a third-party endpoint).create_ingest_pipeline with an inference processor to embed text at index time if needed.index_document or bulk_index; ensure the vector field is populated (from app or pipeline).search tool with a knn query (or hybrid with a keyword query) to run vector search.search with a kNN query and confirm hits and scores.