docker/docs is the source repository for Docker's official documentation website (docs.docker.com). It is a Hugo static-site project with 2,500+ Markdown pages spanning all Docker products — Engine, Compose, Desktop, Hub, Scout, Hardened Images, AI Sandboxes, Model Runner, Docker Agent, security, subscription/billing, and the Extensions SDK — plus generated CLI and API reference sections. The site ships two renderings from one source: conventional HTML/JS for browsers and a plain-Markdown variant (via llms.txt, llms-full.txt, and Accept-header content negotiation) designed for LLMs and AI agents. This dual-output design is the most distinctive architectural feature of the repository. Beneath the content layer sits real engineering. A custom Go module (hack/api-docs) validates multiple OpenAPI 3.2 specifications against a strict JSON Schema dialect, SHA-256-locks upstream spec resources, and emits a single data/api-reference.json model consumed by Hugo. A Hugo content adapter (content/reference/cli/_content.gotmpl) generates ~400 CLI reference pages from structured YAML command trees in data/cli and data/sbx_cli rather than hand-authored Markdown. Upstream project docs (docker/cli, compose, buildx, moby, buildkit, model-runner, docker-agent) are vendored as Go modules with automated sync workflows and PR review guardrails that reject direct edits to generated content. The front end uses Tailwind CSS 4, Alpine.js, Mermaid, Pagefind search, and a self-contained interactive SVG diagram renderer. Build, lint, test, and deploy are fully containerized via docker-bake.hcl, with CI covering link checks, Vale prose linting, rumdl Markdown linting, API-reference regeneration staleness, and Playwright browser checks. The repository is actively maintained: daily and scheduled GitHub Actions keep vendored docs synchronized, a nightly docs scan detects quality issues, Dependabot and stale-issue automation run, and an AI-agent PR review pipeline enforces style and vendoring rules. The main risks are structural rather than stagnation-related: the HTML/Markdown shortcode system must be maintained in two forms (some visual components silently disappear from the Markdown variant), the vendored surface spans six+ upstream repos and depends on disciplined sync, and the rapidly evolving AI product documentation (Sandboxes, Docker Agent, Model Runner, Gordon) creates constant churn. Legacy content such as 2017-era engine release notes and deprecated Compose standalone install pages is retained deliberately as archived history, not as evidence of drift.
The repository maintains a curated list of Persian-language YouTube channels organized by topic. It stores channel data as JSON files in the subjects/ directory, each file representing a category (e.g., technology, programming, education). A Node.js script (scripts/index.js) reads these JSON files, combines them with static header and footer templates, and generates a formatted README.md file using the json2md library. The generated README.md is served via GitHub Pages, and a single HTML file (index.html) fetches the markdown, parses it with a custom, scoped markdown parser, and renders an interactive, searchable interface. The client-side code includes language‑agnostic search (with Persian/Arabic normalization), dynamic section navigation, and responsive UI built with vanilla JavaScript and CSS. The project is kept up to date through GitHub Actions workflows that validate the JSON data, regenerate the README, and run code formatting on every push to master. This ensures the list stays accurate and the generated documentation reflects the latest contributions.