RFC: AI Dev Kit integration (3 tracks)#20
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Three tracks: (1) Genie quality skill for ai-dev-kit, (2) replace hand-rolled SDK calls with databricks-tools-core, (3) Workbench MCP server exposing scoring/analysis as MCP tools.
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Adds a coding agent skill for scoring, analyzing, and optimizing Genie Spaces using a 5-stage maturity model (Nascent → Optimized) with 16 criteria. Includes: - SKILL.md: maturity model, scoring criteria, assessment workflow, API gotchas - optimization.md: stage-by-stage fix patterns with code examples - examples/score-space.py: standalone scorer with formatted report - examples/add-instructions.py: generate instructions from UC metadata - examples/add-joins.py: infer join specs from FK naming patterns This is Track 1 from the AI Dev Kit integration RFC (PR #20). The same skill content lives in ai-dev-kit for when that repo accepts contributions.
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RFC: AI Dev Kit Integration
Exploring how Genie Workbench should integrate with ai-dev-kit across three tracks:
Genie quality skill — Teach ai-dev-kit coding agents how to assess and optimize Genie Spaces using the maturity model. Low effort, ships as a PR to ai-dev-kit.
Replace hand-rolled SDK calls with
databricks-tools-core— The Workbench duplicates UC browsing, SQL execution, and Genie API calls that the shared library already provides. Reduces maintenance, stays in sync.Workbench MCP server — Expose Workbench-specific capabilities (scoring, analysis, fix suggestions) as MCP tools that any coding agent can call.
Key questions for reviewers
databricks-tools-coresupport OBO auth (ContextVar-based, from Databricks Apps)?Full RFC in
docs/rfc-ai-dev-kit-integration.md.