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NOTEBOOK_CONSTITUTION_SUMMARY.md

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NOTEBOOK_CONSTITUTION_SUMMARY.md

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Jupyter Notebook Constitution Amendment Summary

Date: 2025-10-13
Version Change: 1.7.1 → 1.8.0 (MINOR)
Amendment: Added Principle XIII: Jupyter Notebook Discipline (swapped with Streamlit-to-Production Bridge, now XIV)

What Was Added

New Principle XIII: Jupyter Notebook Discipline

A comprehensive governance framework for Jupyter notebooks in the top-level notebooks/ directory that balances:

  • Exploratory freedom for data science experimentation
  • Security requirements to prevent credential leakage
  • Compliance obligations for EU AI Act and security homologation
  • Quality standards for reproducibility and documentation

Key Components

1. Notebook Categorization

Three distinct categories with different governance levels:

  • notebooks/exploratory/: Rapid experimentation, not subject to SpecKit workflow
  • notebooks/documentation/: Tutorials and examples, subject to documentation standards
  • notebooks/production-adjacent/: Model evaluation and compliance reporting, subject to EU AI Act requirements

2. Security Requirements (NON-NEGOTIABLE)

  • No hardcoded credentials or sensitive data
  • nbstripout pre-commit hook to remove outputs
  • .gitignore patterns for notebook execution artifacts
  • Security review before public publication
  • GDPR compliance for data sources

3. Quality Standards

  • Reproducibility: dependency specifications required
  • Documentation: purpose, author, data sources, runtime requirements
  • Version control: outputs stripped before commit
  • Code quality: ruff linting where practical
  • Cell organization: structured narrative with markdown

4. Integration with SpecKit Workflow

  • Exploratory: Not required to follow SpecKit, but insights must be captured when productionized
  • Documentation: Referenced in spec.md and quickstart guides
  • Production-adjacent: Documented in plan.md research section

5. EU AI Act Compliance

Production-adjacent notebooks for high-risk AI systems must document:

  • Model training data characteristics
  • Evaluation metrics and validation results
  • Risk assessment findings
  • Model selection audit trail
  • Technical documentation for homologation dossier

6. Tooling Standards

  • nbstripout: Remove outputs before commit
  • nbconvert: Convert to scripts/docs
  • papermill: Parameterize and execute programmatically
  • ruff: Lint notebook code (via nbqa)
  • uv: Manage dependencies in monorepo

7. Migration to Production

Clear 5-step process:

  1. Extract code to packages/ or apps/
  2. Document migration in feature spec
  3. Archive exploratory notebooks
  4. Retain production-adjacent for compliance
  5. Follow Principle XI for production implementation

Why This Matters

Problems Solved

  1. Security Risk: Prevents accidental credential commits in notebooks
  2. Compliance Liability: Ensures notebooks support EU AI Act documentation requirements
  3. Technical Debt: Provides clear migration path from exploration to production
  4. Irreproducibility: Mandates dependency management and documentation
  5. Audit Trail: Establishes governance for model decisions and evaluations

Alignment with Existing Principles

  • Principle I (EU AI Act): Production-adjacent notebooks support compliance documentation
  • Principle III (Security Homologation): Security requirements prevent credential leakage
  • Principle IV (Open Source): Guidance on what can be published publicly
  • Principle X (Python-First): Notebooks align with Python-first culture
  • Principle XI (SpecKit): Integration with specification-driven workflow

Template Updates

✅ Completed

  • constitution.md: Added Principle XIII (Jupyter Notebook Discipline) with full governance framework, swapped with Streamlit-to-Production Bridge (now XIV)
  • plan-template.md: Updated Constitution Check section with correct principle ordering (XIII: Notebooks, XIV: Streamlit)

No Changes Required

  • spec-template.md: No principle-specific references
  • tasks-template.md: No principle-specific references

Next Steps (Follow-up TODOs)

When you create the notebooks support feature, you should:

  1. Create feature spec: specs/002-jupyter-notebook-support/
  2. Pre-commit hooks: Add nbstripout configuration
  3. Gitignore patterns: Add notebooks/**/*.ipynb output patterns
  4. Notebook templates: Create templates for each category
  5. Ruff configuration: Add notebook linting via nbqa
  6. Migration guide: Document notebook-to-production patterns
  7. Directory structure: Create notebooks/{exploratory,documentation,production-adjacent,archive}/

Rationale for MINOR Version Bump

This is a MINOR (1.8.0) rather than PATCH because:

  • New principle added: Expands governance scope to notebooks
  • New mandatory requirements: Security and quality standards for notebooks
  • New tooling standards: nbstripout, papermill, nbqa
  • Material guidance expansion: Comprehensive framework, not just clarification

Not MAJOR because:

  • No breaking changes: Existing projects without notebooks are unaffected
  • Backward compatible: Adds requirements only for new notebook usage
  • No principle removals: All existing principles remain intact

Suggested Commit Message

docs: amend constitution to v1.8.0 (add Principle XIV: Jupyter Notebook Discipline)

- Add comprehensive governance framework for notebooks/ directory
- Establish security requirements (nbstripout, no credentials)
- Define notebook categories (exploratory, documentation, production-adjacent)
- Integrate with SpecKit workflow and EU AI Act compliance
- Update plan-template.md Constitution Check with Principle XIV
- Provide clear migration path from notebooks to production code

Rationale: Jupyter notebooks are essential for AI/ML experimentation but
require governance to prevent security risks, compliance liabilities, and
technical debt accumulation.

Questions for Clarification

Before creating the feature spec, consider:

  1. Notebook execution environment: Should notebooks run in the shared .venv or isolated environments?
  2. CI/CD integration: Should notebooks be executed in CI for validation?
  3. Notebook templates: What starter templates would be most valuable (data exploration, model evaluation, compliance reporting)?
  4. Integration with existing tools: How should notebooks interact with apps/ and packages/?
  5. Compliance tooling: Do you need automated compliance checks for production-adjacent notebooks?

Constitution Version: 1.8.0
Amendment Status: ✅ Complete
Ready for Feature Spec: Yes