Remove nbqa in favor of ruff's native Jupyter support (v0.6.0+).
Replace papermill with nbconvert due to uv dependency resolution issues.
Also remove S105/S106 ignores to enforce better security practices.
- Update pyproject.toml to use ruff v0.12.12 with native notebook support
- Replace papermill with nbconvert for notebook execution
- Remove nbqa from all dependencies and pre-commit hooks
- Update GitHub Actions workflows to use ruff directly
- Remove hardcoded password ignores for better security
- Update documentation to reflect simplified setup
- Add dummy package structure for hatchling build system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Configure lychee for notebook link validation
- Set up GitHub workflow for PR and scheduled checks
- Exclude API endpoints and localhost from checks
- Add PR comment integration for broken links
- Add nbstripout to clean notebook outputs
- Configure nbqa with ruff for notebook linting
- Add ruff for Python file formatting
- Add custom hooks for model and notebook validation
- Add notebook-quality.yml with papermill execution testing
- Add claude-model-check.yml for model validation
- Add security-scan.yml for secret detection
- Implement tiered testing (full for maintainers, mock for external)
- Add allowed_models.py that fetches latest models from Anthropic docs
- Implement 24-hour cache to avoid excessive requests
- Fall back to hardcoded list if fetch fails
- Add check_models.py to validate model usage in notebooks
- Add validate_notebooks.py for security and structure checks
- Update .gitignore for cache files
Update timestamp generation to snap to bucket boundaries to ensure
consistent data retrieval, as partial dates may not return data.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Comprehensive guide to programmatically accessing Claude API usage and cost data for custom dashboards, cost monitoring, and usage analysis.
Features:
- Basic usage and cost tracking
- Time granularity and filtering options
- Grouping and breakdowns
- Priority Tier analysis
- Pagination for large datasets
- Error handling best practices
- Practical alerting examples
