Spec to Sellable: A Markdown-Driven Template Engine for Spreadsheets and Branded PDFs

Abstract This paper documents a template engine that converts a markdown specification (YAML frontmatter plus markdown body) into either a styled .xlsx spreadsheet or a branded PDF checklist. The engine has been used to produce 100+ digital products across categories (financial calculators, business checklists, engineering specifications, craft pricing tools) for direct sale on hibranwar.com and partner platforms. The five components handle: spec-to-xlsx conversion, spec-to-PDF conversion, a roadmap-style PDF builder, a financial calculator engine with dual currency support, and a full-featured branded spreadsheet builder. The contribution is a single-source authoring model for digital products: edit one markdown file, regenerate two formats. What this deposit contains paper.md — the methodology paper (the citable scholarly artifact) supplementary.zip — bundled working source code for 5 component workflows covered by this paper, each with its own README, Python source, configuration, sample input, CITATION.cff, and LICENSE README.md — entry point CITATION.cff — machine-readable citation LICENSE (MIT for code) and LICENSE-DOCS (CC BY 4.0 for documentation) Production Results Templates produced: 100+ across 10 categories Categories: keuangan-pribadi, bisnis-warung, franchise, gig-karir, legalitas, pajak, ekspor, craft, engineering, publishing Time per new template: ~30 minutes (write spec, regenerate) Limitations The spec format is opinionated. It does not support arbitrary worksheet layouts; for those, write openpyxl directly. Spec-to-PDF does not support tables in the body; use spec-to-xlsx for tabular content. Reproducibility Five MIT-licensed Python modules. Each ships with a working sample spec and example output. Companion Papers This deposit is one of ten flagship records in the Practitioner Publishing Stack series. Each flagship is a methodology paper plus the relevant working source as supplementary material. The series: I. Markdown to multi-format compile pipeline (.docx, .pdf, .epub) with consistent typography. (slug: f01-pub-compile-stack, domain: PUB) II. Detection and removal of AI writing signatures from manuscripts before publication. (slug: f02-aiq-zero-signature-pipeline, domain: AIQ) III. End-to-end pipeline from literature search to Zenodo-published, DOI-indexed academic artifact. (slug: f03-res-research-to-doi, domain: RES) IV. Six python-docx utilities for format normalization, heading conversion, and AI-signature cleanup. (slug: f04-doc-production-pipeline, domain: DOC) V. Visual design system: color tokens, typography, charts, diagrams, and AI cover composition. (slug: f05-vis-leather-and-steel-design-system, domain: VIS) VI. (this paper) — Markdown-driven template engine producing branded spreadsheets and PDF checklists for direct sale. VII. Per-channel specification workflow for distribution across seven publishing platforms. (slug: f07-dst-multi-platform-distribution, domain: DST) VIII. Course-materials pipeline: markdown syllabus to branded PDF and per-session DOCX. (slug: f08-edu-course-materials, domain: EDU) IX. Seven-phase book production methodology with hard quality gates and 18-day average cycle time. (slug: f09-meta-book-lifecycle, domain: META) X. Architectural overview of an independent publishing operation that produced 558+ titles. (slug: f10-meta-practitioner-publishing-stack, domain: META) Author Ibrahim Anwar (Hibranwar) ORCID: 0009-0006-0425-4923 Wikidata: Q138856145 Web: hibranwar.com Affiliation: PT Hibrkraft Kreasi Indonesia (Cileungsi, Bogor, Indonesia) License The methodology paper, configuration, and sample data are released under CC BY 4.0. The Python source code in supplementary.zip is released under the MIT License. Citation If you use this work, please cite via the DOI minted on this Zenodo record. A machine-readable CITATION.cff ships in the deposit. About the Practitioner Publishing Stack The Practitioner Publishing Stack documents an independent publishing operation by Ibrahim Anwar that produced 558+ titles across nonfiction books, public-domain translations, academic papers, and digital templates, distributed across seven platforms, with a single human as the bottleneck. Average end-to-end cycle time per title: 18 days. Operator headcount: 1.