Quick Prompt: Enhance Your Workflow with AI Prompt Management
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
Quick Prompt is an innovative browser extension designed to streamline your interactions with AI tools by providing robust prompt management and quick input capabilities. It allows users to create, organize, and rapidly insert predefined prompts into any web input field, significantly boosting productivity and efficiency. Built with TypeScript, Quick Prompt offers a seamless experience for managing your AI prompts.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
Quick Prompt is an innovative browser extension designed to streamline your interactions with AI tools by providing robust prompt management and quick input capabilities. Developed by wenyuanw, this project helps users create, organize, and rapidly insert predefined prompts into any web input field, significantly boosting productivity and efficiency. Built with TypeScript, Quick Prompt offers a seamless experience for managing your AI prompts.
For those who prefer a desktop experience, a compatible Quick Prompt Raycast plugin is also available, allowing for smooth data migration.
Installation
Getting started with Quick Prompt is straightforward. You can install it via the Chrome Web Store, download a pre-built package from GitHub Releases, or build it from source.
From Chrome Web Store
The easiest way to install is directly from the Chrome Web Store:
From GitHub Releases
Alternatively, you can download the latest version from GitHub:
- Visit the GitHub Releases page.
- Download the latest built extension package.
- Unzip the downloaded file.
- Follow the instructions below to load the unpacked extension in your browser.
From Source
For developers, you can build Quick Prompt from its source code:
git clone https://github.com/wenyuanw/quick-prompt.git
cd quick-prompt
pnpm install
pnpm build
Refer to the official GitHub repository for detailed build instructions for other browsers like Firefox.
Installing a Built Extension
- Chrome / Edge:
- Open
chrome://extensionsoredge://extensions. - Enable "Developer mode".
- Click "Load unpacked".
- Select the
.output/chrome-mv3/directory from your project.
- Open
- Firefox:
- Open
about:debugging. - Click "This Firefox".
- Click "Load Temporary Add-on".
- Select the
manifest.jsonfile within the.output/firefox-mv2/directory of your project.
- Open
Examples
Quick Prompt offers several intuitive ways to access and manage your prompts:
- Quick Trigger: Simply type
/pinto any text input field on a webpage to bring up the prompt selector. - Keyboard Shortcut: Use
Ctrl+Shift+P(Windows/Linux) orCommand+Shift+P(macOS) to open the prompt selector anywhere. - Saving Prompts:
- Shortcut: Select any text, then use
Ctrl+Shift+S(Windows/Linux) orCommand+Shift+S(macOS) to quickly save it as a new prompt. - Right-Click Menu: Select text, right-click, and choose "Save this prompt" from the context menu.
- Shortcut: Select any text, then use
- Variable Support: Prompts can include variables in the format
{{variable_name}}. When you select such a prompt, a pop-up will appear, allowing you to input specific values for these variables before insertion. - Data Management: Easily export your entire prompt library to a JSON file for backup or import a JSON file to restore or merge prompts.
- Notion Sync: Synchronize your prompt library with a Notion database for centralized management.
The extension provides a clean and user-friendly interface for both prompt selection and management, as showcased in the repository's preview images.
Why Use It
Quick Prompt stands out as an essential tool for anyone frequently interacting with AI models. Its key benefits include:
- Enhanced Productivity: Drastically reduce repetitive typing by quickly inserting complex or frequently used prompts.
- Centralized Management: Keep all your AI prompts organized in one place, with support for categories, tags, and search functionality.
- Customization and Flexibility: Define custom prompts with variables, adapting them to various scenarios without needing to rewrite them each time.
- Data Portability: Secure your prompt library with easy export/import features, ensuring your data is always accessible and backed up.
- Seamless Integration: Works directly within your browser, integrating smoothly into your existing workflow.
- Notion Integration: For users leveraging Notion, the synchronization feature offers an unparalleled level of organization and accessibility for their prompt library.
Links
- GitHub Repository: https://github.com/wenyuanw/quick-prompt
- Chrome Web Store: https://chromewebstore.google.com/detail/quick-prompt/hnjamiaoicaepbkhdoknhhcedjdocpkd
- Raycast Plugin: https://github.com/wenyuanw/quick-prompt-raycast
Related repositories
Similar repositories that may be relevant next.

Colibri: Run 744B GLM-5.2 MoE on Consumer Machines with Pure C
July 11, 2026
Colibri is an innovative project that enables running the massive 744B-parameter GLM-5.2 Mixture-of-Experts (MoE) model on consumer-grade machines with as little as 25GB of RAM. It achieves this remarkable feat through a pure C engine with zero dependencies, streaming model experts from disk on demand. This allows users to interact with a frontier-class LLM without requiring expensive GPU hardware.

Lamini: The Official Python Client for Generative AI API
July 6, 2026
Lamini is the official Python client and SDK designed to interact with the Lamini API, enabling developers to create their own Generative AI applications. It provides a straightforward interface for integrating powerful AI capabilities into Python projects. This package simplifies the process of building and deploying generative AI solutions.

RL4LMs: A Modular RL Library for Fine-tuning Language Models
July 6, 2026
RL4LMs is a powerful and modular reinforcement learning library designed to fine-tune language models to human preferences. It offers easily customizable building blocks for training, including on-policy algorithms, reward functions, and metrics. Thoroughly tested and benchmarked, RL4LMs supports a wide range of NLP tasks and models.

torchtune: PyTorch Native Library for LLM Post-Training and Experimentation
July 5, 2026
torchtune is a PyTorch native library designed for authoring, post-training, and experimenting with Large Language Models (LLMs). It offers hackable training recipes, simple PyTorch implementations of popular LLMs, and best-in-class memory efficiency. Please note: torchtune is no longer actively maintained as of 2025.
Source repository
Open the original repository on GitHub.