research-documentationlisted

Use when a repository produces measurements, benchmarks or experiments that need writing up as research rather than as a guide, or when any document in it has to be authored in LaTeX and compiled to PDF. What counts as research and what does not, the docs/research layout, the shape of a study document, the evidence rules (every number traceable, reproduction stated as a command, validate the instrument before trusting its readings, negative results get written up), when a study graduates to a LaTeX paper or a Beamer deck, how a non-study document borrows the house style without claiming to be research, how Mermaid diagrams reach a PDF from a single source, how a second language edition is published, and the shape of the GitHub Actions workflow that builds any of them. Bundled assets: the study template, the paper template and the Beamer theme.
konradcinkusz/architecture-standards · ★ 0 · Data & Documents · score 72

Install: claude install-skill konradcinkusz/architecture-standards

# Scientific research documentation **Read [`references/00-RESEARCH-DOCUMENTATION.md`](references/00-RESEARCH-DOCUMENTATION.md) before applying any of this.** That file is the standard; everything below it is a summary to help you decide whether this skill applies and to check your work afterwards. Reference-architecture principles: P14. Bundled templates: [`assets/TEMPLATE.md`](assets/TEMPLATE.md), [`assets/PAPER-TEMPLATE.tex`](assets/PAPER-TEMPLATE.tex), [`assets/BEAMER-THEME.sty`](assets/BEAMER-THEME.sty). ## What this standard covers - What counts as research (and what doesn't) - Where research lives in a repository - The shape of a study - The evidence rules - From study to paper (LaTeX) - Presenting work as slides (Beamer) - Documents that borrow the house style without being studies - Diagrams in a PDF - Publishing a document in more than one language - Building the PDF in CI - Relationship to the rest of the standards - Worked example ## What counts as research (and what doesn't) A **study** asks a question whose answer wasn't known before the work was done, and answers it with evidence: measurements, verified computations, reproducible experiments, or systematic analysis of committed code. Examples: benchmarking an algorithm against its theoretical complexity, verifying an implementation against independently derived expected values, characterizing a system's behavior under a sweep of inputs, root-causing a discrepancy between a model's specification and its i