Real file from a public repo · synced Aug 26, 2026
databricks-model-serving
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage AI Gateway rate limits; discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
Works with
Install in three steps
- 1
Download the ZIP
One file per agent, plus INSTALL.md with the exact paths below.
- 2
Claude Code: copy SKILL.md into your skills folder
.claude/skills/databricks-model-serving/SKILL.md # this project ~/.claude/skills/databricks-model-serving/SKILL.md # every project
- 3
Other agents: use the matching file
skill.mdc goes in Cursor’s .cursor/rules/. AGENTS.md works as GitHub Copilot instructions, a legacy .cursorrules, or a Codex / Gemini CLI instructions file.
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