skills: promote databricks-vector-search to stable

.claude-plugin/plugin.json

@@ -19,6 +19,7 @@
     "pipelines",
     "dabs",
     "serverless",
+    "vector-search",
     "data-engineering"
   ],
   "skills": "./skills/"

README.md

@@ -69,6 +69,7 @@ Stable skills shipped from [`skills/`](./skills/):
 - **databricks-model-serving** — Model Serving endpoint management, AI Gateway, traffic config.
 - **databricks-pipelines** — Lakeflow Spark Declarative Pipelines (formerly DLT) for batch and streaming.
 - **databricks-serverless-migration** — Migrate classic-compute workloads to serverless compute.
+- **databricks-vector-search** — Vector Search endpoints + indexes for RAG and semantic search.
 
 ## Experimental Skills
 

experimental/README.md

@@ -41,7 +41,6 @@ See the root [README](../README.md) for details on the stable install path.
 - **databricks-agent-bricks** - Knowledge Assistants, Genie Spaces, Supervisor Agents
 - **databricks-mlflow-evaluation** - End-to-end agent evaluation workflow
 - **databricks-unstructured-pdf-generation** - Generate synthetic PDFs for RAG
-- **databricks-vector-search** - Vector similarity search for RAG and semantic search
 
 ### 📊 Analytics & Dashboards
 - **databricks-aibi-dashboards** - Databricks AI/BI dashboards (with SQL validation workflow)

manifest.json

@@ -406,7 +406,7 @@
       "version": "0.0.1"
     },
     "databricks-vector-search": {
-      "description": "Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings. Use when building RAG applications, semantic search, or similarity matching. Covers both storage-optimized and standard endpoints.",
+      "description": "Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns",
       "files": [
         "SKILL.md",
         "agents/openai.yaml",
@@ -417,8 +417,8 @@
         "references/search-modes.md",
         "references/troubleshooting-and-operations.md"
       ],
-      "repo_dir": "experimental",
-      "version": "0.0.1"
+      "repo_dir": "skills",
+      "version": "0.1.0"
     },
     "databricks-zerobus-ingest": {
       "description": "Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.",

scripts/skills.py

@@ -33,6 +33,7 @@
     "databricks-model-serving": {"plugin_keyword": "model-serving"},
     "databricks-pipelines": {"plugin_keyword": "pipelines"},
     "databricks-serverless-migration": {"plugin_keyword": "serverless"},
+    "databricks-vector-search": {"plugin_keyword": "vector-search"},
 }
 
 

experimental/databricks-vector-search/SKILL.md → skills/databricks-vector-search/SKILL.md

@@ -1,10 +1,15 @@
 ---
 name: databricks-vector-search
-description: "Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings. Use when building RAG applications, semantic search, or similarity matching. Covers both storage-optimized and standard endpoints."
+description: "Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns"
+metadata:
+  version: "0.1.0"
+parent: databricks-core
 ---
 
 # Databricks Vector Search
 
+**FIRST**: Use the parent `databricks-core` skill for CLI basics, authentication, and profile selection.
+
 Patterns for creating, managing, and querying vector search indexes for RAG and semantic search applications.
 
 ## When to Use