Add analytics dashboard design proposal document#20
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minhlucvan merged 7 commits intomainfrom Mar 13, 2026
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Proposes a new data sources + dashboard templates feature for notebookmd that bridges the gap between heavyweight BI tools (Metabase, PowerBI, Dash) and raw print() — enabling AI agents to connect to databases, CSVs, and APIs, auto-discover schemas, and generate full dashboards as plain Markdown. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
… positioning Adds lightweight semantic layer concept (metric definitions, report metadata headers), cross-report linking/indexing, and detailed competitive analysis vs Evidence.dev, Streamlit, and Metabase based on market research. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
Major rewrite based on deep research into Metabase (no markdown export), DuckDB (ideal embedded query engine), MindsDB (universal data adapter), MCP ecosystem (340% growth, 500+ servers), and the AI analytics landscape (Databricks Genie, Snowflake Cortex, ThoughtSpot Spotter, Hex Agent). Key positioning shift: notebookmd is the "last-mile rendering layer" that complements DuckDB/MindsDB/MCP rather than competing with BI tools. DuckDB-first strategy replaces custom connector sprawl. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
Strip away tool comparisons and infrastructure concerns. Focus on the core positioning: notebookmd is a code-first representation layer that instantly enables any AI agent to produce structured Markdown analytics. Agent writes Python → notebookmd renders → analytics happens. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
Fundamental reframe: the dashboard layer sits ABOVE notebookmd and thinks about data sources, metrics, joins, dimensions, and slicing. Visualization is the last step, not the first. New Dashboard API: dash.source(), dash.metric(), dash.join(), dash.slice(), dash.trend(), dash.breakdown(), dash.compare(), dash.show() — agents think in analytical concepts, not widget calls. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
Rewrite proposal to directly map to Metabase/Looker/PowerBI/Cube data models. Same concepts (dimensions, measures, joins, tiles, filters) but in Python, for agents, with Markdown output. Adds interop layer: import from Metabase/Looker/Cube, export back to them. One data model, every BI platform, Markdown output. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
Exact field-level mappings between notebookmd analytics model and: - Metabase: MBQL aggregations, field refs, dashcards, parameters, display types - Looker: LookML views, dimensions, measures, dimension_groups, explores - Cube: cube schema, dimensions, measures, joins, REST /load API - PowerBI: semantic model tables, DAX measures, relationships, slicers Includes step-by-step import/export flow for Metabase API integration. https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7
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Summary
This PR adds a comprehensive design proposal for extending notebookmd with a complete analytics dashboard layer. The proposal outlines how notebookmd can evolve from a report generation library into a lightweight, agent-native BI platform with data source connectors, auto-discovery, dashboard templates, and refresh capabilities.
Changes
docs/analytics-dashboard-proposal.md— A detailed 568-line design document covering:n.connect(),n.source_profile(),n.auto_dashboard())Type of Change
Testing
N/A — This is a design proposal document, not implementation code.
Checklist
https://claude.ai/code/session_01JRHMQxstocN8hCt41458n7