# prompt-engineering

Modular AI prompt framework for everyday tasks

Fabric is an open-source framework that organizes AI prompts into reusable patterns for solving everyday tasks like summarizing content, explaining code, extracting insights from videos, and generating social media posts. Written in Go with support for 20+ AI providers including OpenAI, Claude, Gemini, and Ollama, it runs from the command line and can serve as a REST API. With 40,000+ GitHub stars, Fabric bridges the gap between AI capabilities and practical workflow automation.

Context engineering patterns for AI coding assistants

Context Engineering Intro is an open-source repository by Cole Medin providing structured context engineering patterns for AI coding assistants. Built around Claude Code, it includes .claude command files, PRP templates, and the WISC framework for managing AI context in coding sessions. The repo shows how to structure project context and rules so AI assistants produce reliable, architecture-aware code. With 13K+ GitHub stars, it is a go-to reference for context-first AI coding.

Open-source LLMOps platform for prompt management and evaluation

Agenta is an open-source LLMOps platform that combines prompt engineering playgrounds, prompt version management, LLM evaluation, and observability in a unified interface. It supports 50+ LLM models with side-by-side prompt comparison, A/B testing, human evaluation workflows, and OpenTelemetry-native tracing. Self-hostable with 4,000+ GitHub stars.

LLM evaluation and prompt engineering platform

Braintrust is an LLM evaluation platform for testing, scoring, and iterating on AI applications with dataset-centric regression testing. Features a prompt playground for rapid experimentation, automated evaluation with custom scorers and LLM judges, dataset management for building test suites from production data, and detailed tracing for debugging. Supports A/B testing of prompts, comparison across model providers, and CI/CD integration for automated quality gates on LLM outputs.

AI-driven development workflow template

A template system that bootstraps AI-driven development workflows for your projects. Provides structured workflows, templates, and configurations for integrating AI agents into your development process. Reduces setup time by giving teams a proven starting point for organizing AI-assisted coding, task management, and quality assurance in new and existing repositories.

Curated collection of agent skills and capabilities

An opinionated project scaffolding tool that generates AI-ready codebases with pre-configured CLAUDE.md files, git hooks, and CI/CD templates. Ensures new projects follow best practices for AI-assisted development from day one, including structured prompts, context files, and workflow configurations that help AI coding agents understand and navigate the codebase effectively.

Toolkit for spec-driven development with AI

GitHub's official toolkit for spec-driven development. Write specifications in natural language and let AI coding agents implement them with structure, consistency, and traceability. Bridges the gap between product requirements and AI-generated code by providing a standardized format that agents can follow reliably across complex projects.

TypeScript AI agent standard library

Standard library of AI tools and integrations for TypeScript-based agents. Works with any AI SDK and includes ready-made integrations for search, web scraping, email, and other common tool patterns. Saves developers from rebuilding common agent capabilities from scratch, providing well-tested, type-safe building blocks for rapid AI agent development.

LLM testing and evaluation toolkit

Open-source tool for testing, evaluating, and red-teaming LLM applications. Promptfoo lets developers define test cases, run prompts across multiple models and configurations, and score outputs with built-in metrics like factuality, relevance, and toxicity. Includes red-teaming for jailbreak and hallucination detection plus CI/CD integration for automated prompt regression testing.

Programming — not prompting — LLMs

Declarative framework from Stanford University for programming language models rather than prompting them. DSPy treats LLM interactions as programmable modules with input-output signatures and uses optimization algorithms to automatically compile these modules into effective prompts or fine-tuned weights, replacing brittle prompt strings with structured, modular AI software.

Type-safe LLM function builder

BAML is a domain-specific language by BoundaryML for building reliable AI workflows and agents through schema engineering. It turns prompt engineering into a structured, type-safe discipline by letting developers declaratively define function schemas, validate LLM responses, and version prompts without fragile JSON parsing or boilerplate. BAML reframes prompt engineering as schema definition, making AI workflows testable and maintainable across models.

Structured generation for LLMs

Outlines is an open-source Python library for structured text generation that guarantees LLM outputs conform to a defined schema or format. It constrains the model's token selection at each step so only tokens leading to valid output are considered, eliminating fragile post-processing. Supports multiple-choice constraints, regex patterns, JSON Schema, and type-safe Pydantic models — helping teams extract reliable structured data from any LLM.

Structured LLM outputs with validation

Instructor is the most popular Python library for extracting structured, validated data from large language models, with over 3 million monthly downloads and ports across Python, TypeScript, Go, Ruby, Elixir, and Rust. It uses Pydantic models to define output schemas and automatically handles validation, retries, and error correction when the LLM output does not match. Instructor patches existing client libraries instead of replacing them, preserving full access to the underlying API.

OpenAI's custom chatbot builder and GPT Store

Create personalized GPT assistants with custom instructions, knowledge files, and tool integrations including browsing, DALL-E, and code interpreter. Publish to the GPT Store or keep private with no coding required. Enables anyone to build specialized AI assistants for specific domains, workflows, or audiences using OpenAI's consumer-friendly builder interface.

Claude's inline code and document generation tool

Claude's built-in capability to generate and render interactive artifacts — code, documents, SVGs, React components, and HTML — directly inline within the conversation. No setup required. Turns Claude from a text-only assistant into a creative tool that can produce runnable applications, visualizations, and interactive prototypes during natural conversation.

Curated Claude Code resources

Official Anthropic-curated list of Claude Code tips, CLAUDE.md templates, hooks, MCP servers, and community tools. Essential reference for Claude Code users looking to optimize their workflow. Covers everything from initial setup and configuration best practices to advanced patterns like custom hooks and multi-agent orchestration with the Claude Code CLI.

Community cursor rules directory

Community-maintained collection of .cursorrules files that customize Cursor IDE's AI behavior for specific frameworks, languages, and project types. Define coding conventions, preferred libraries, architectural patterns, and style guidelines that the AI follows consistently. Popular rules exist for Next.js, React, Python, TypeScript, Tailwind, and more. Hosted on cursor.directory with 1-click installation. Essential for getting consistent, project-aware AI completions in Cursor.