Awesome GitHub RepositoriesTemplate-Based Code Generators

curlconverter is a browser-based tool and JavaScript library that transforms curl commands into equivalent source code across more than 30 programming languages and HTTP client libraries. It parses curl command arguments into an abstract syntax tree and generates idiomatic code by applying per-language templates, making it a curl command transpiler rather than a simple converter. The tool operates entirely client-side without any server round-trips, ensuring all conversion happens privately in the browser without transmitting data externally. It can also function as a drop-in curl replacement

sqlboiler is a database-first ORM generator for Go that analyzes an existing database schema to produce strongly typed structures and query helpers. It functions as a schema-driven code generator, transforming database tables and relationships into executable Go source code. The project distinguishes itself through a type-safe query builder that uses chainable modifiers to construct SQL statements, eliminating the need for raw string concatenation. It utilizes customizable text templates to generate source code, allowing for the aliasing of schema entities and the creation of custom templates

Hygen is a code generator CLI and interactive template engine that scaffolds new files and injects code into existing ones using project-local templates. It operates as a Node.js code generator library that can be embedded inside custom binaries for tailored workflows, and also functions as a project scaffolding tool for bootstrapping new projects or folders from remote template repositories. The tool discovers templates by scanning a project's _templates directory at runtime, mapping folder and file names directly to generator commands and actions. It collects user input through interactiv

This project provides methodologies and guides for structured prompt engineering, generative workflows, and specialized image generation strategies. It serves as a framework for optimizing inputs to large language models across coding, writing, and analysis tasks, as well as a library of techniques for controlling diffusion models. The project distinguishes itself through an AI-driven software design framework that converts business requirements into technical architectures and code using domain-driven prompting. It also implements generative AI workflow patterns that use sequential prompt pi