Controlled workbook-to-board-pack pipeline
Built and tested a workbook-to-HTML reporting process, then developed the contracts and controls for reuse across different pack types.
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The problem
Manual pack production did not guarantee source reconciliation, fresh evidence or clear interpretation by non-technical readers.
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My role
Built a historical pack pipeline around checked workbook data and a reusable data contract.
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Where it stands
A historical pack passed 71 pipeline tests and 15 business checks and produced an offline report of roughly fifty-eight pages. The wider reusable factory remains under development; these recorded checks do not certify its current state.
Results and evidence
Business impact
Makes the route from checked data to a readable decision pack explicit and reusable.
How I did it
- 01
Applied control-total, source-freshness, offline-rendering and reader-comprehension checks to that pack.
- 02
Separated the checked data contract, view specification and renderer. Engagement analysis and acquisition judgement remain outside the factory.
Tools and working context
A stack-agnostic data contract feeding an offline, confidential-by-design pack generator.
- Tools
- Python · Pure-Python HTML/SVG renderer · YAML/JSON contracts · pytest · Headless-Chrome PDF
- Business area
- Board reporting · Decision support
- Skill type
- Automation · Finance
Why I made these choices
- Failed closed when source evidence was stale or unverified.
- Required extracted figures to equal the source statement, preventing internally consistent but incorrect packs.
What I learned
- A real-reader comprehension gate catches decision failures that arithmetic checks cannot.
What comes next
- Add further pack types and widen the external-source reconciliation anchors.