Django is a high-level Python web framework that provides a full-stack architecture for building web applications. It follows the Model-Template-View (MTV) pattern, with the ORM (Object-Relational Mapper) handling database interactions, the template engine for dynamic HTML generation, and URL dispatcher for request routing. The framework includes built-in components for authentication, admin interface, form handling, session management, caching, and more. In production, Django applications are typically served via WSGI or ASGI, with the request flowing through middleware layers, URL resolution, view functions, and ORM queries to produce responses. The framework's management commands (django-admin/manage.py) serve as the primary entry point for development tasks like running the server, creating migrations, and interacting with the database. Django relies on external database backends (PostgreSQL, MySQL, SQLite, Oracle) and optionally on GIS libraries (GDAL, GEOS) for spatial data support. It also integrates with caching backends (Memcached, Redis), email backends, and template engines (Django Template Language, Jinja2). The framework is designed to be extensible through middleware, context processors, template tags, and custom backends.
Hugo is a production-grade static site generator written in Go. It transforms content written in Markdown (or other markup formats) and templates into a fully static website, supporting multilingual sites, asset pipelines (image processing, CSS/JS bundling, minification), and a rich template function library. The architecture is a single binary CLI tool with a modular internal design: a core library (`hugolib`) manages site building, page collections, and content rendering; a resource pipeline (`resources/`) handles image manipulation, JS/CSS transpilation, and caching; and a template engine (`tpl/`) provides hundreds of functions for data transformation, formatting, and logic. Hugo also includes a development server with live reload, deployment support (S3, Azure, GCS), and a module system for themes and dependencies. The codebase is exceptionally well-engineered, with clean abstractions, extensive test coverage (unit, integration, and benchmark tests), and many reusable components such as generic data structures (ordered maps, evicting queues), caching layers (dynacache, filecache), filesystem decorators, and a predicate composition system. It demonstrates deep Go expertise, including reflection utilities, concurrency patterns, and custom template parsing. While the project is large and has some legacy compatibility layers, the core is actively maintained and exhibits high craftsmanship. Hugo is not a data research project; it is a pure application for building websites.
Twig is a mature, feature-rich template engine for PHP that compiles templates to optimized PHP code. It employs a lexer-parser-compiler pipeline to transform template source into an abstract syntax tree (AST), which is then compiled to executable PHP. The engine emphasizes security through automatic escaping and a sandboxing system, and extensibility via a robust extension system that allows adding custom functions, filters, tests, and token parsers. Twig supports template inheritance, macros, and expressive syntax with features like null-safe operators and arrow functions. The architecture is modular and follows several well-established patterns: a plugin-based expression parser, a decorator-pattern caching system, a visitor pattern for AST manipulation, and an attribute-driven extension registration mechanism (using PHP 8+ attributes). The codebase demonstrates careful attention to extensibility, security, and performance, with comprehensive test coverage and active maintenance. While the project manages significant backward compatibility (notably preparing for Twig 4.0), it maintains a clean separation of concerns and provides reusable components that could be adapted for other projects, such as its chainable cache, expression parser registry, and sandbox security policy.
Kami is a locally installed AI-agent skill and plugin for professional document generation, aimed at Claude Code, Codex, Cursor, and MCP-capable agents. It is not a hosted service: agents invoke it through natural language, and the skill orchestrates a deterministic pipeline that validates structured content, fills self-contained HTML templates, renders PDFs via WeasyPrint, optionally exports PPTX or Marp slides, and runs a battery of quality gates before declaring a document deliverable. The repo also ships a zero-dependency MCP stdio server exposing tools such as kami_templates, kami_render, kami_check, and kami_screenshot.
oapi-codegen is a production-grade OpenAPI 3.0/3.1 code generator for Go that produces idiomatic server stubs, typed HTTP clients, and data models. It supports multiple Go web frameworks (Chi, Echo, Fiber v2/v3, Gin, Gorilla, Iris, and stdlib net/http) and generates both plain and strict server interfaces with middleware composition, request validation, and error handling. The generator is configurable via YAML files and offers features like import mapping for multi-package specs, OpenAPI overlay support, webhook generation, streaming responses, custom struct tags, and configurable schema-merging behaviors (v1/v2/v3) for allOf/anyOf/oneOf composition. The architecture is a library with a CLI wrapper (`cmd/oapi-codegen`). The core generation pipeline loads and validates an OpenAPI spec, gathers schemas from components/operations, resolves naming collisions, merges composed schemas, prunes unused components, and renders Go source files via user-overridable text/template files. The codebase is extensively tested with hundreds of generated regression tests covering edge cases in schema composition, parameter serialization, content types, and framework-specific behavior. The generator also embeds the original spec as a compressed base64 blob in generated packages for runtime access. Key reusable components include a robust naming resolution algorithm with collision handling, a configurable struct tag generation system with eager validation, a dead-code elimination module for pruning unused schema components, and standalone security providers (Bearer token, API key, Basic auth) for generated clients. The project maintains compatibility layers for schema-merging behavior versions and deprecated APIs, ensuring smooth upgrades while preserving existing generated code.
docker-gen is a Go CLI tool that generates configuration files from Docker container metadata using Go templates. It connects to the Docker daemon, lists containers based on configurable filters (exposed ports, labels, etc.), and renders templates with container data (IPs, ports, environment variables, labels, networks). The generated output can be written to files or stdout, and docker-gen can notify services by running commands or sending signals to containers when the output changes. It supports watching Docker events for automatic regeneration with configurable debouncing. The architecture is a single binary with well-separated internal packages: config parsing, Docker client abstraction, container context extraction, template engine with custom functions (filtering, grouping, sorting, nginx-specific quoting/HSTS validation), and a generator that orchestrates event listening, signal handling, and file generation. The codebase is mature, extensively tested, and demonstrates high craftsmanship in its generic template helper functions (deepGet, where, groupBy, sortBy) which are reusable beyond this project. It is primarily used as part of the nginx-proxy ecosystem to auto-generate reverse proxy configurations, but can be adapted for any file generation from Docker metadata.
EJS (Embedded JavaScript) is a mature, widely-used templating library for Node.js and browsers. It compiles template strings (using `<% %>` tags) into JavaScript functions for server-side or client-side rendering. The core API provides `compile`, `render`, and `renderFile` methods, with support for includes, custom delimiters, caching, async rendering, and Express integration. The library is dual-published as CommonJS and ES Modules, and ships with a CLI tool for rendering templates from the command line. The codebase is well-engineered with a clean separation of concerns: `lib/esm/ejs.js` contains the main template engine, `lib/esm/utils.js` provides helper functions (including prototype-pollution-hardened shallow copy and XML escaping), and `lib/esm/parseargs.js` is a reusable CLI argument parser that was extracted to replace a Jake dependency. The build pipeline uses TypeScript to compile ESM to CJS, browserify for the browser bundle, and uglify-js for minification. Tests are comprehensive (Mocha) and include unit tests for the argument parser, prototype pollution resistance, and ecosystem compatibility with bundlers like Rollup, Webpack, Vite, and Deno. The library demonstrates strong engineering practices: it explicitly avoids prototype pollution by using null-prototype objects, validates option names to prevent code injection, and supports strict mode and destructured locals as alternatives to the deprecated `with` statement. The CLI parser is a pure, well-tested module that handles edge cases like attached short values, grouped booleans, error collection, and prototype-safe option names. While the core engine uses `new Function` (standard for JS template engines) and defaults to `with`, these are documented trade-offs with opt-out flags. Overall, EJS is a solid, professionally maintained project with a clean architecture and high reusability of its utility modules.
Render is a Go library that provides a flexible rendering layer for web applications, supporting multiple output formats including JSON, XML, HTML, text, and binary data. It abstracts away the complexities of marshaling data, setting appropriate content types and headers, and executing HTML templates with support for layouts, partials, and custom template functions. The library is designed to work with any Go web framework by accepting an http.ResponseWriter. The architecture centers around a Render struct that holds configuration and compiled templates, with different renderer types (JSON, XML, HTML, etc.) implementing a common Engine interface. Notable features include automatic template recompilation in development mode, buffer pooling for efficient HTML rendering, streaming JSON output options, and a lock-free implementation by default with optional mutex locking for thread safety. The library also provides filesystem abstraction to support loading templates from disk, embedded files, or custom asset functions.
pdfme is a TypeScript monorepo for template-driven PDF generation and manipulation. In production it does three things: (1) generates PDF bytes from a JSON template plus input data using a plugin-based schema renderer (packages/generator + packages/schemas), (2) ships React widgets — Designer, Form, Viewer — for visually editing templates, filling forms, and previewing output (packages/ui), and (3) exposes a CLI (packages/cli) for headless generation, validation, diagnostics, and PDF conversion, plus a page-manipulation library (packages/manipulator) and an experimental JSX authoring layer (packages/jsx). The low-level PDF engine is a heavily extended, vendored fork of pdf-lib (packages/pdf-lib) with custom AcroForm appearance providers, SVG drawing, and font subsetting; fontkit provides glyph metrics, bwip-js renders barcodes, and @napi-rs/canvas powers Node-side raster conversion. A playground web app and Docusaurus documentation site are included as consumers. The core data flow is: Template + inputs are validated by shared Zod schemas (packages/common), then each schema plugin's pdfRender implementation draws into a pdf-lib document while a dynamic-layout engine computes split ranges, overflow expansion, and table/list pagination across pages. The same Plugin contract (pdf/ui/propPanel/icon) drives both PDF rendering and the WYSIWYG interface, so custom field types register once and work everywhere. Notable engineering: a sandboxed expression evaluator with prototype-pollution and XSS guards (common/expression.ts), SSRF-safe URL fetching with IPv4/IPv6/mapped-address checks (common/helper.ts), a from-scratch UAX #14 line-breaking engine with Japanese kinsoku handling (schemas/text/lineBreak.ts), and a safe inline-Markdown-to-rich-text parser. Weaknesses: the vendored pdf-lib fork contains dormant/commented API stubs and diverges from upstream, image-snapshot tests are brittle across rendering environments, several modules use module-level mutable caches, and some package version pins are aspirational (e.g., TypeScript ^7 in website). Repo-wide version coordination relies on internal packages pinned at 0.0.0 with * workspace deps. Overall the codebase is professional, well-tested (unit, integration, e2e, visual snapshots, security regression tests), actively maintained with CI publishing, and is an application — not a data/research project.
Android Stem is a Gradle plugin that resolves placeholders in Android XML string resource files at compile time. It scans `res/values/` directories (and library dependencies) for strings containing placeholders like `${app_name}`, then replaces them with actual values defined in other strings. The plugin supports multi-language (`values-es/`, `values-fr/`), multi-flavor (product flavors), and recursive template resolution with circular dependency detection. It integrates with the Android Gradle Plugin (AGP) variant API, registering three cacheable tasks per build variant: `RawStringCollector` (collects all string resources into a protobuf binary), `GatherStringTemplates` (identifies templates and their values, outputting JSON), and `ResolvePlaceholders` (performs the actual placeholder substitution and writes resolved XML files to `build/generated/stem/`). The plugin is configured via an `androidStem` block in `build.gradle.kts`, allowing customization of placeholder delimiters and localized-only template inclusion. It is published on the Gradle Plugin Portal and has a comprehensive test suite covering unit, integration, and concurrency scenarios.
go-swagger is a long-lived CLI and library for OpenAPI/Swagger 2.0 tooling, centered on code generation. The `swagger` binary (cmd/swagger) exposes commands for validate, diff, expand, flatten, mixin, serve, init, version, and generate; `swagger generate` produces Go server, client, model, operation, markdown, and CLI code from a Swagger spec. The generation pipeline loads and validates a spec through the go-openapi stack, flattens it, builds an intermediate representation of models/schemas/operations, then renders `.gotmpl` templates into Go source, finishing with a custom lightweight Go formatter and import sorting. The generated servers and clients depend on go-openapi runtime packages and can embed the spec and serve API docs. The tool also shells out to the Go toolchain at runtime, which is why the Docker image includes Go.
This repository is the official source for .NET Docker images published by Microsoft. It generates, builds, tests, and publishes container images for .NET SDK, runtime, ASP.NET Core, monitor, and other components across a wide matrix of .NET versions (8.0, 9.0, 10.0, 11.0 preview) and base operating systems (Alpine, Ubuntu, Debian, Azure Linux, Windows Nano Server/Server Core) for multiple architectures (amd64, arm32, arm64). The architecture is template-driven: Dockerfiles are generated from parameterized templates in `eng/dockerfile-templates/` using a custom Cottle-based engine, and version updates are automated via a CLI tool in `eng/update-dependencies/` that fetches build information from the .NET Build Asset Registry (BAR) and creates pull requests. The repository also includes extensive test infrastructure (xUnit tests, approval baselines) and CI/CD pipelines (Azure DevOps) that orchestrate image building, signing, scanning, and publishing to Microsoft Container Registry (MCR) and Docker Hub. Sample applications demonstrate usage patterns, and Kubernetes manifests illustrate deployment scenarios. The codebase is well-maintained, with active commits and issue triage, and exhibits low technical drift due to automated dependency management.
HMPL.js is a lightweight JavaScript library for server-oriented HTML templating. It compiles templates with block helpers (e.g., `{{#request ...}}`) into functions that fetch HTML from a server, sanitize it via DOMPurify, and render it into the DOM. The library supports indicators, memoization, intervals, and status binding, and uses JSON5 for expressive attribute parsing. The core is a single TypeScript module (`src/main.ts`) that exports `compile`, `stringify`, `createWarning`, and `createError`. The repository also contains a comprehensive documentation site built with Astro + Starlight (including Vue interactive components) and a Jekyll blog. The codebase is well-tested with Mocha, Sinon, nock, and jsdom, and has CI/CD via GitHub Actions. It is primarily a frontend library but includes robust build and test infrastructure.
PPT Master is a Python-based application designed to generate natively editable PowerPoint (PPTX) presentations from diverse source formats including PDF, DOCX, Markdown, and URLs. The system operates as a CLI tool and AI agent skill, orchestrating a workflow that converts source content into SVG intermediates before rendering them into native DrawingML shapes within the PPTX file. This approach prioritizes editability over flattened image exports, allowing users to modify text and shapes directly in PowerPoint. The architecture relies heavily on external AI APIs for content generation and image creation, with a local Flask server providing live preview and confirmation capabilities. The codebase functions as a presentation engineering toolkit, featuring bidirectional conversion between PPTX and SVG, template management, and asset handling. It enforces strict design constraints via machine-readable 'spec_lock' files to maintain visual consistency across generated decks. The application is structured as a modular skill within AI IDEs (Claude Code, Cursor), allowing developers to invoke presentation generation via chat commands. While the core conversion logic is robust, the system's stability is intrinsically linked to the availability and schema of external AI providers and the specific behavior of the python-pptx library. This repository represents a hybrid of application engineering and data pipeline logic. While the primary output is a software artifact (the PPTX file), the internal processing involves significant data transformation (document parsing, image generation, XML manipulation). The extensive example suite serves as both documentation and regression testing for the conversion engine, demonstrating the tool's capability across technical, business, and artistic domains.
The Obsidian Web Clipper is a browser extension that enables users to clip web content and save it as markdown notes in their Obsidian vault. It features a modular architecture with background scripts, content scripts, popup UI, and settings pages, plus a CLI component. The extension provides highlighting, reader mode, template-based formatting with variables and filters, AI-powered content processing, and cross-browser support for Chrome, Firefox, and Safari. Data flows from user interaction through the popup UI to content scripts that extract and process webpage data, which is then formatted via a template engine and saved to Obsidian. The system relies on webextension-polyfill for cross-browser compatibility, defuddle for content extraction, and various UI libraries for functionality.
San is a Baidu-developed, production-grade frontend component framework that enables building reactive UIs with a Vue/React-like model, but using custom template syntax and a standalone runtime. It provides a template parser (producing an ANode AST), a reactive data system (Data class, Proxy-based this.d), directives (s-if, s-for, s-bind, s-show, s-html, s-ref, s-transition), slots (named, scoped, dynamic), computed properties, filters, lifecycle hooks, two-way binding, and server-side rendering hydration. The library is monolithic but modularly organized, targeting browsers from IE8 upward with conditional compilation. It is actively maintained with frequent releases and extensive test coverage, but carries legacy complexity and a self-contained build toolchain (AMD module loading, custom feature blocks). The ecosystem includes companion libraries (san-router, san-store, san-cli) and TypeScript typings.
Inja is a header-only C++ template engine that renders Jinja2-like templates using JSON data. It provides a full-featured templating language including variables, loops, conditionals, template inheritance, includes, custom callbacks, whitespace control, and HTML autoescape. The library is built on top of nlohmann/json and requires C++17. It is distributed as a single-include header or via package managers. The architecture is clean: an Environment class orchestrates parsing (lexer + parser into AST) and rendering (evaluator with built-in and user-defined functions). Extensive unit tests and benchmarks ensure correctness and performance. The library is actively maintained with CI across multiple compilers and platforms.
Hamlit is a high-performance implementation of the Haml templating language for Ruby. It focuses on speed optimizations through reduced string concatenation, static analysis via Ripper/Temple, and C-optimized attribute building. The engine uses the Temple framework to compile Haml templates into efficient Ruby code, with additional optimizations like string splitting and dynamic merging to minimize runtime object allocations. The architecture is modular, consisting of a core engine, parser (generated from Haml source), compiler, and filter system. It provides integration points for Rails (via Railtie) and framework-agnostic use through Tilt. A command-line interface is included for template compilation and rendering. The project also includes a C extension (hamlit.c and hescape.c) to accelerate performance-critical operations, with fallbacks to pure Ruby for compatibility with JRuby and TruffleRuby. While Hamlit's performance edge has diminished since Haml 6 incorporated similar optimizations, the codebase remains a well-crafted example of template engine engineering. It demonstrates strong practices in balancing performance with portability, and several components (such as the escape utilities, string splitter, and dynamic merger) are suitably reusable in other contexts. The project is actively maintained, with recent commits indicating ongoing support.
This is an Obsidian plugin that integrates large language models (LLMs) directly into the note-taking environment. It provides a template-driven text generation system using Handlebars with custom async helpers, supports multiple LLM providers (OpenAI, Anthropic, Google Gemini, Mistral, Ollama, Perplexity, HuggingFace, Together AI, MiniMax, Azure, and custom endpoints), and offers content extraction from PDF, audio, images, YouTube, RSS, and web pages. The plugin includes a community template marketplace, auto-suggest, an overlay toolbar, a playground for testing templates, and extensive Obsidian integration (commands, views, settings, CodeMirror extensions). Architecturally, it is a client-side monolithic plugin that communicates directly with external LLM APIs and the npm registry for package management. The codebase is large (300+ files) and well-organized into providers, extractors, UI components, and services, but it shows some drift due to hard-coded model lists and deprecated provider support (e.g., Google PaLM). The async Handlebars helpers and generic caching utility are highlight components that could be extracted for reuse in other projects.
html-bundler-webpack-plugin is a webpack plugin that processes HTML templates and bundles associated assets (CSS, JS, images, etc.) into output HTML files. It treats HTML as a webpack entry point, resolves dependencies via webpack's module system, and updates asset references to hashed output URLs. The plugin supports multiple template engines (Pug, Handlebars, EJS, Nunjucks, etc.), asset inlining, subresource integrity, hot module replacement, and code splitting. Architecture consists of a main plugin class, loader infrastructure, and modular preprocessors. It integrates with webpack's compilation hooks for asset extraction and HTML modification. Examples demonstrate usage with frameworks like React, Vue, and CSS tools like TailwindCSS.