Abstract The reproducibility crisis in computational research is fundamentally structural: research artifacts are scattered across disconnected tools—LaTeX editors, Jupyter notebooks, ad-hoc shell scripts—with no enforced mechanism to keep code, data, and manuscript synchronized. Studies have shown that most published findings are false positives, replication rates in psychology hover around 36%, and only 24% of 1.4 million Jupyter notebooks can be successfully re-executed. Existing tools address fragments of this problem: workflow managers (Snakemake, Nextflow, CWL) orchestrate computation; literate programming systems (Quarto, Jupyter Book, R Markdown, Overleaf, OpenAI Prism) render documents; data versioning tools (DVC) track artifacts—but none enforces cross-cutting quality standards as architectural invariants. template/ applies the principle of Infrastructure as Code to the research lifecycle, making the manuscript, test suite, and provenance chain version-controlled, deterministically buildable, and independently verifiable. It is built on a Two-Layer Architecture that separates ${module_count} reusable infrastructure subpackages (~${total_infra_python_files} Python modules, validated by ~${infra_test_count_approx} tests) from self-contained project workspaces, connected by a YAML-declared pipeline (${pipeline_stages_declared} stages; default full ${pipeline_stages_default_full})-based build pipeline progressing from environment sanitization through test execution (with a Zero-Mock testing policy enforcing 90% project-level and 60% infrastructure-level coverage via real filesystem operations and subprocess invocations), analysis script invocation, Pandoc/XeLaTeX rendering, SHA-256 cryptographic hashing with steganographic watermarking, structural PDF validation, and LLM-assisted review. A Documentation Duality standard equips every directory with both human-readable README.md and machine-readable AGENTS.md files, while each infrastructure module additionally carries a SKILL.md—a structured skill descriptor aligned with the Model Context Protocol—enabling AI agents to locate and invoke module capabilities without hallucinating API signatures. Scalability is demonstrated across three heterogeneous exemplars under projects/—optimization (template_code_project, ${project_template_code_project_test_count} tests), prose (template_prose_project, ${project_template_prose_project_test_count} tests), and AutoResearch readiness (template_autoresearch_project, ${project_template_autoresearch_project_test_count} tests)—representing guaranteed control-positive layouts for code-centric, prose-centric, and retrieval-centric workflows at 90%+ project coverage alongside 60%+ infrastructure gates. All three share identical pipeline stages without cross-project coupling. This manuscript adds a complementary reflexive artifact: authored from projects_in_progress/template (${project_template_template_test_count} tests) until promotion, using the same analysis and render path and injecting counters from repository introspection. The fact that these words, metrics, and figures were generated by the pipeline they describe demonstrates self-documenting capacity: rendered through the DAG, validated without mocks, optionally watermarked. A comparative analysis against nine peer tools across fourteen dimensions positions template/ as integrating fourteen distinctive enforcement capabilities—testing thresholds, cryptographic provenance, steganographic watermarking, multi-project management, MCP-aligned skill descriptors, Zero-Mock policy, orchestration through publication—in one repository. Code is released under the Apache License 2.0 at github.com/docxology/template; the work remains open-ended. --- Associated artifacts GitHub release: v1.0.5 (https://github.com/docxology/template_template/releases/tag/v1.0.5) DOI: https://doi.org/10.5281/zenodo.20419055 Zenodo: https://zenodo.org/records/20419055 PDF SHA-256: 5f200f33ce0dffdc1ba6911372bf1b18dca484dc248ef47e627935d5b409fbab