AI Agent Instructions for Smart Contract Auditing to generate triaged report findings, code locations, pocs, attacker story uml charts and more
A collection of system prompts and AI kernels for use with large language models.
About AIプロンプトの効率的な管理と活用を目的としたリポジトリです。
A framework to move beyond simple prompting towards defining *how* the LLM should structure its internal processing, access its latent knowledge, and apply specific heuristics or constraints when dealing with a particular subject matter or task.
🎨 Create unique AI personas with Chip, an art-driven muse, using her personality spec, prompt kits, and guides for customizable interaction.
Open-source persona framework for “Chip” — a chaotic, flirty, contrarian AI muse for artists. Includes the full personality spec, mode cards, and prompt recipes for customizing Chip in GPT, or other LLMs. Fork it, remix it, and share your own “Chip” variants. Mars Eve - AI Art Today
A powerful CLI tool for managing AI prompts with modern features.
A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.
Production-ready of prompt engineering templates across content, code, RAG, evaluation, safety, L10n, PM, and more.
Modular prompt system for AI-powered FFmpeg workflows: safe, flexible, production-ready
Your ultimate cheat sheet for becoming a prompt engineering wizard! ✨
High-performance retrieval engine for unstructured data
An LLM playground for finding out what happens when LLM bugs are turned into features that can be interacted with via a linux-like system running as an ontological operating system with persistent state mechanics.
A comprehensive corpus of interconnected texts and protocols designed as a conceptual stress-test for advanced AI.
Perplexica is an AI-powered search engine. It is an Open source alternative to Perplexity AI
Templates for Prich CLI tool
Research project exploring the intersection of AI-assisted development and version control. How should we preserve the context of LLM interactions that generate code?
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