Build Notebook

Build notebook from spec

Read a <date>_<pipeline_name>_analysis_plan.md artifact and assemble a marimo notebook with all charts.

Parse $ARGUMENTS:

• spec-path (optional): path to the analysis_plan.md file. If omitted, look for *_analysis_plan.md in the working directory. If multiple found, ask the user and stop.

Step 1: Read analysis_plan.md

Parse the analysis plan file for:

• Connection section: pipeline name, dataset name, destination type
• Chart N sections: each chart's question, type, SQL code, and altair code
• Count the total number of charts to assemble

If the analysis plan file is missing or has no charts, tell the user to run explore-data first and stop.

Step 2: Assemble notebook

Generate <pipeline_name>_dashboard.py. Read references/notebook-patterns.md for the complete notebook structure, cell templates, and naming conventions before generating. Every chart cell must end with _chart on a bare line, then return — without the bare expression line, nothing renders.

Marimo patterns

For general marimo cell structure, reactivity, and best practices, fetch https://github.com/marimo-team/skills/blob/main/skills/marimo-notebook/SKILL.md. For SQL-specific patterns in marimo, fetch https://github.com/marimo-team/skills/blob/main/skills/marimo-notebook/references/SQL.md. For dlt-dashboard-specific templates, see references/notebook-patterns.md.

Step 3: Validate

Run marimo's linter to catch common mistakes:

uvx marimo check <pipeline_name>_dashboard.py

If validation fails:

1. Read the error output — marimo check reports specific issues (missing returns, variable conflicts, import errors).
2. Fix the reported issues in the notebook file.
3. Re-run uvx marimo check until it passes.

Step 4: Ensure dependencies

The notebook requires pandas, numpy, and altair which are not installed by dlt[hub]. In a dlthub workspace (dlthub init) they are already declared in pyproject.toml — running uv sync installs them.

Before launching, check if they are available. If any are missing, ask the user how they want to install them:

• If they are declared in pyproject.toml (workspace scaffolded by dlthub init): run uv sync.
• Otherwise: run uv add pandas numpy altair to add them to pyproject.toml.

Also add marimo if not already installed, and ibis-framework[duckdb] if any chart uses ibis.

Do NOT install packages without user confirmation.

Step 5: Launch

After validation passes, offer to launch in browser or skip.

If yes:

uv run marimo edit <pipeline_name>_dashboard.py --no-token

Tell the user the notebook is running (default: localhost:2718).

Regeneration

When re-invoked after iteration (see workflow.md): re-read the full analysis_plan.md and regenerate the entire notebook file, then validate and relaunch.

Troubleshooting

marimo check fails with variable conflicts

Two cells export the same variable name. Fix: follow the naming conventions in references/notebook-patterns.md.

marimo check fails with import errors

A dependency is missing from the environment. Install it with uv add <package> and re-check.

Notebook runs but charts are empty

The SQL query returns no rows. Common causes:

1. Filter is too restrictive — check where clauses.
2. Column names don't match schema — verify against get_table_schema.
3. Table is empty — check row_counts.

dlt.attach fails in notebook

Pipeline name is wrong or pipeline hasn't been run. Run dlthub local pipeline info <name> to verify.

Example

Input: 2026-03-10_orders_pipeline_analysis_plan.md with 2 charts (Monthly Revenue Trend + Revenue by Category)

Output: orders_pipeline_dashboard.py with 2 data cells + 2 chart cells, structured per references/notebook-patterns.md.

Validation: uvx marimo check orders_pipeline_dashboard.py → passes Launch: uv run marimo edit orders_pipeline_dashboard.py --no-token