template

template addresses the reproducibility challenge in computational research: traditional workflows lack systematic tracking and verification across experimental steps, environments, data versions, and output documents.

Its core capability is a 10-stage DAG pipeline that orchestrates Python computation, pandoc document conversion, pytest testing, and LaTeX typesetting into an automated workflow, with cryptographic-level provenance via SHA-256 hashing and steganographic PDF watermarking. Multi-project workspace support enables concurrent management of several research projects.

The project embeds AGENTS.md and SKILL.md files throughout its directory structure, making it friendly to AI coding tools like Cursor and AI agents, and usable as a foundational template for AI-assisted research.

It suits academic researchers and computational science teams requiring strict reproducibility and traceability; it may be over-engineered for lightweight personal notes or single-document scenarios without provenance needs. GitHub repository: https://github.com/docxology/template.