A skills set and methodology for coding agents: clarify the task, plan, write tests, then code. The agent works with more discipline.
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224 living projects — from agent frameworks to prompt-injection defense. Each comes with our own note: why it matters, who it is for and how to start in three steps.
Stars, forks and activity refresh daily via the GitHub API · updated October 6, 2026
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A service that turns any website into clean Markdown or structured data for models. It can search, scrape, crawl a whole site and even click through a page.
A small Microsoft utility that converts PDF, Word, PowerPoint, Excel and other files into Markdown that is easy to hand to a language model.
An open coding agent for the terminal with a desktop app. It is not tied to one provider: plug in whichever models you have.
A toolkit for spec-driven development: principles and a spec first, then a plan and tasks, and only then code. Works with several coding agents.
OpenAI's lightweight coding agent that runs in the terminal and works on your project's code. Written in Rust.
Categories
Agent frameworksFrameworks and orchestration: what agents and multi-agent systems are built from.23 repositoriesCoding agents & CLIAgents that write code in the terminal and editor, and the methods for working with them.21 repositoriesMCPThe Model Context Protocol spec, official SDKs and the best servers.20 repositoriesOpen models & inferenceOpen weights, local runs, fast inference and fine-tuning.38 repositoriesRAG & vector DBsSearch over your own data: document parsing, crawlers, vector databases, knowledge graphs.19 repositoriesEvals & observabilityHow to measure a model or agent and see what happens inside it.17 repositoriesPrompting & contextPrompts, structured output, skills and context engineering.16 repositoriesVoice, video, multimodalSpeech recognition and synthesis, voice agents, images and video.19 repositoriesAutomationNo-code workflows, browser and computer-use agents.15 repositoriesAwesome lists & learningCourses, books and awesome lists worth starting with.21 repositoriesSecurityPrompt injection, guardrails, red teaming and agent scanners.15 repositoriesMissing your favorite?Suggest a repository — we will review it and add it if it earns a spot.Suggest a repo →
Catalog
12 repositories
Formerly Awesome ChatGPT Prompts: a huge community library of prompts plus an interactive book on prompting. You can deploy your own private copy.
A collection of system prompts and tool descriptions from popular AI products: Cursor, Devin, Windsurf, Lovable, Manus and others. Useful for studying how pros build them.
A toolkit for spec-driven development: principles and a spec first, then a plan and tasks, and only then code. Works with several coding agents.
A detailed guide to prompt engineering: techniques from few-shot to chains of thought, papers, lectures and notebooks. Recently extended with context engineering and agents.
A framework where instead of hand-tuning prompts you describe a program from modules, and the system tunes prompts and examples against a quality metric.
Anthropic's interactive prompting course: 9 chapters with exercises where you can try things right away and see how Claude's answer changes.
Twelve principles for building LLM apps good enough to put in customers' hands: own your prompts, own your context, small agents, explicit control flow.
A command-line tool for testing prompts and agents: describe cases in a config and compare models. Also does red teaming and vulnerability scanning. Plugs into CI.
A command-line tool and Python library for dozens of models: OpenAI, Claude, Gemini and local ones. Prompts and responses are logged to SQLite, with embeddings, schemas and tools built in.
A dedicated language for describing LLM calls as typed functions: prompt, response schema and tests live together and compile into a client for your language.
Compresses long prompts and context up to 20x with little quality loss by dropping low-value tokens. Saves money and speeds up responses.
An automated prompt injection scanner for your own LLM applications. It sends attack prompts and checks whether the model gave in.
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