A CLI tool to convert your codebase into a single LLM prompt with source tree, prompt templating, and token counting.
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
ClickPrompt - Streamline your prompt design, with ClickPrompt, you can easily view, share, and run these prompts with just one click. ClickPrompt 用于一键轻松查看、分享和执行您的 Prompt。
A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.
A playground of highly experimental prompts, tools & scripts for machine intelligence models from Apple, DeepSeek, OpenAI, Anthropic, Meta, Mistral, Google, xAI & others. Created & maintained by Alex Bilzerian.
Lovable.dev community: https://www.skool.com/lovable-vibe-coding-4202 MVP in days: https://aidevelopers.tech/
A flexible UI script to help create and expand on prompts for generative AI art models, such as Stable Diffusion and MidJourney. Get inspired, and create.
Context management for long-context LLMs, agents, and vibe coding. Instantly build context for an entire repo, selected files, folders, and GitHub issues to generate structured AI-XML context with real-time token counting.
A user-friendly tool for generating high-quality text prompts for AI image generation models like Midjourney, DALL-E, Stable Diffusion, and more. Our Hugging Face demo attracts over 2,500 users monthly!
A simple prompt generator API for Stable Diffusion / Midjourney / Dall-e based in Python
Prompt Generator for Stable Diffusion/Midjourney on GPT-2 models
Prompt-Promptor is a python library for automatically generating prompts using LLMs
PowerShell made Posh
Image boorus API powered pony prompt helper extension for A1111 / Forge and ComfyUI
promt collection for special use case, tested on chatGPT and Bard
markdown parser and prompt generator tools for text2image prompts
Turn your images into detailed and descriptive text prompts with AI
A Stable Diffusion Prompt Generator based on ChatGPT
GeneticPromptLab uses genetic algorithms for automated prompt engineering (for LLMs), enhancing quality and diversity through iterative selection, crossover, and mutation, while efficiently exploring minimal yet diverse samples from the training set.
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