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Prompt Engineering

Prompt engineering is the practice of designing and optimizing prompts to effectively guide large language models (LLMs) and generative AI systems. It improves AI responses by using clear instructions, relevant context, examples, and constraints to achieve accurate and reliable outputs.

Here are 23,206 public repositories matching this topic...

system_prompts_leaks

Leaked system prompts of ChatGPT, Claude, Gemini and more. The hidden system prompt of every popular chatbot, in full, every version since May 2025. Also Anthropic Claude Code, OpenAI Codex, Google Antigravity, xAI Grok, Meta Muse, Moonshot Kimi, Alibaba Qwen, Z.ai GLM ZCode, MiniMax, Cursor, Perplexity, DeepSeek and 30+ others. Updated daily.

  • Updated Nov 6, 2026
  • Python

410 Claude Code skills & agent skills & plugins (120+ Agents, custom commands, 410+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8 more coding agents — engineering, marketing, product, compliance, C-level advisory, research, business operations, commercial & finance, and your daily productivity skills.

  • Updated Nov 5, 2026
  • Python
mlflow

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

  • Updated Nov 7, 2026
  • Python

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, DeepSeek, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.

  • Updated Nov 7, 2026
  • TypeScript

🚀 A curated collection of AI engineering resources, roadmaps, tools, and projects covering LLMs, RAG, AI Agents, MCP, fine-tuning, evaluations, and production AI systems.

  • Updated Oct 21, 2026