Contents

• Verification Enforcement
• Why Execute to ipynb?
• Execution Commands
• Inspection Methods
• Quick Failure Check
• Read Tool for Debugging
• Common Patterns
• Debugging Workflow

Debugging Executed Notebooks

This skill covers inspecting executed .ipynb files to debug runtime errors, regardless of how the notebook was created (marimo, jupytext, or plain Jupyter).

If debugging within a /ds workflow, first read .planning/LEARNINGS.md for pipeline context and .planning/PLAN.md for task expectations.

Verification Enforcement

IRON LAW: NO 'NOTEBOOK WORKS' CLAIM WITHOUT TRACEBACK CHECK

Before claiming ANY notebook executed successfully, you MUST:

1. EXECUTE the notebook to ipynb with outputs
2. CHECK for tracebacks (Quick Failure Check section)
3. READ the ipynb file with Read tool if errors found
4. VERIFY all cells have execution_count (not null)
5. INSPECT outputs for warnings/unexpected behavior
6. CLAIM success only after all verification passes

This is not negotiable. Skipping traceback checks is NOT HELPFUL — the user opens a notebook that throws errors on first run.

Rationalization Table - STOP If You Think:

Drive-Aligned Framing

Red Flags - STOP Immediately If You Think:

• "Let me run marimo/jupytext and assume it worked" → NO. Execute to ipynb and CHECK outputs.
• "The notebook ran last time, so it still works" → NO. Fresh execution EVERY time.
• "I can tell from the code that it's correct" → NO. Code inspection ≠ runtime verification.
• "Just a small change, can't break anything" → NO. Small changes cause big failures.

Verification Checklist

Before claiming "notebook works":

Execution:

• Execute notebook to ipynb format
• Use --include-outputs flag (for marimo)
• Verify output file created successfully
• Verify output file is non-empty

Traceback Check:

• Run quick failure check: jq -r '.cells[].outputs[]?.text[]?' | grep "Traceback"
• Check error count: jq '[.cells[].outputs[]? | select(.output_type == "error")] | length'
• Use Read tool to inspect full context if errors found

Cell Execution:

• Verify all cells have execution_count (no null values)
• Check execution order is sequential (no out-of-order cells)
• Verify no cells skipped due to prior failures

Output Inspection:

• Verify critical outputs (not just absence of errors)
• Check expected results present (dataframes, plots, metrics)
• Verify no warnings that indicate problems
• Check no unexpected NaN/None/empty results

Claim success only after:

• All checks pass: declare "notebook executed successfully"

Gate Function: Notebook Verification

Apply this verification sequence for every notebook debugging task:

1. EXECUTE → Run to ipynb with outputs
2. CHECK   → Quick traceback/error count check
3. READ    → Full inspection with Read tool if errors
4. VERIFY  → All cells executed, outputs as expected
5. CLAIM   → "Notebook works" only after all gates passed

Never skip any gate. Each gate catches different failure modes.

Why Execute to ipynb?

Converting and executing notebooks to ipynb captures:

• Cell outputs and return values
• Tracebacks with full context
• Execution order and cell IDs

This makes debugging much easier than reading raw .py source.

Execution Commands

# Export marimo notebook to ipynb with outputs
marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs

# Convert jupytext to ipynb and execute with outputs
jupytext --to notebook --output - script.py | papermill - output.ipynb

# Execute existing ipynb notebook to capture outputs
papermill input.ipynb output.ipynb

Inspection Methods

Verdict: Use Read for debugging/inspection, jq for scripting/CI.

Quick Failure Check

# Check for tracebacks in notebook outputs
jq -r '.cells[].outputs[]?.text[]?' notebook.ipynb | grep "Traceback"

# Count error outputs in notebook
jq '[.cells[].outputs[]? | select(.output_type == "error")] | length' notebook.ipynb

Read Tool for Debugging

The Read tool renders ipynb with errors inline after the failing cell:

<cell id="MJUe">raise ValueError("intentional error")</cell>

Traceback (most recent call last):
  File "/path/to/notebook.py", line 5, in <module>
    raise ValueError("intentional error")
ValueError: intentional error

<cell id="vblA">y = x + 10  # depends on x, not the error cell</cell>

Benefits:

• Errors appear immediately after the cell that caused them
• Cell IDs visible for cross-referencing
• Full traceback with line numbers
• No JSON parsing needed

Common Patterns

Find the Failing Cell

Use the Read tool to inspect the notebook and locate tracebacks:

# Read notebook to find traceback location inline after failing cell
Read __marimo__/notebook.ipynb

Check Cell Execution Count

Identify cells that did not execute:

# Find cells with null execution_count (not executed)
jq '.cells[] | select(.execution_count == null) | .source[:50]' notebook.ipynb

Extract All Errors

Gather all error outputs from executed cells:

# Extract error tracebacks from all cells
jq -r '.cells[].outputs[]? | select(.output_type == "error") | .traceback[]' notebook.ipynb

Debugging Workflow

1. Execute notebook with outputs captured:

   bashCopy

   # Export marimo notebook to ipynb format with all outputs
   marimo export ipynb nb.py -o __marimo__/nb.ipynb --include-outputs
   

2. Run quick failure check:

   bashCopy

   # Check if execution produced tracebacks
   jq -r '.cells[].outputs[]?.text[]?' __marimo__/nb.ipynb | grep -q "Traceback" && echo "FAILED"
   

3. Inspect notebook using Read tool:

   bashCopy

   # Read the full notebook to identify failing cells and their errors
   Read __marimo__/nb.ipynb
   

4. Fix source code and re-run to verify