Agent skill · software engineering · aiskillstore
azure-search-documents-ts
Build search applications with vector, hybrid, and semantic search capabilities.
Why this skill is useful
Provides specific TypeScript code examples and configurations for implementing Azure's vector and semantic search capabilities that the AI wouldn't reliably generate on its own.
What it needs
Requires @azure/identity, @azure/search-documents installed locally. Requires azure account access. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Azure AI Search SDK for TypeScript Build search applications with vector, hybrid, and semantic search capabilities. Installation Environment Variables Authentication Core Workflow Create Index with Vector Field Index Documents Full-Text Search Vector Search Hybrid Search (Text + Vector) Semantic Search Filtering and Facets Autocomplete and Suggestions Batch Operations Key Types Best Practices 1. Use hybrid search - Combine vector + text for best results 2. Enable semantic ranking - Improves relevance for natural language queries 3. Batch document uploads - Use uploadDocuments with arrays, not single docs 4. Use filters for security - Implement document-level security with filters 5. Index incrementally - Use mergeOrUploadDocuments for updates 6. Monitor query performance - Use includeTotalCount: true sparingly in production When to Use This skill is applicable to execute the workflow or actions described in the overview. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment-specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/azure-search-documents-ts