Projects sharing features with Vibe Coding Cn

BAML is a prompt engineering framework and LLM client generator that defines AI prompts as type-safe functions. It serves as a structured data extraction tool and workflow orchestrator, transforming unstructured model responses into strongly typed objects using a custom schema language and alignment algorithms. The project distinguishes itself by using a compiler to generate language-specific boilerplate code for API communication and output parsing. It features a dedicated environment for designing complex prompt templates with conditional logic and reusable snippets, and employs genetic alg

Vibe-coding is an agentic workflow manager and AI coding orchestrator designed to guide autonomous agents through software development. It serves as a development framework that organizes the process of building software using large language models through structured planning, iterative validation, and a defined cycle of implementation. The project distinguishes itself through a focused context management system and project memory bank, which uses dedicated files to maintain consistent architectural context across sessions. It employs constraint-based guidance to enforce project-specific codi

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

This project is an AI agent workflow framework and development toolkit designed for AI-driven software engineering. It provides a system of modular instructions, prompt libraries, and standardized routines to orchestrate complex engineering sequences and automate the decomposition of plans into technical tasks. The system differentiates itself through advanced context management and prompt engineering, using state compression and handoff documents to preserve conversation history between different AI sessions. It employs a structured library of prompt skills and high-signal trigger words to e

Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and domain-driven development methodologies. It provides a library of standardized interaction strategies designed to improve the consistency, accuracy, and logical reasoning of large language model outputs. By applying these patterns, users can translate complex business scenarios into structured domain models and technical specifications. The project distinguishes itself by integrating domain-driven design principles directly into the prompting workflow. It utilizes techniques such as

ChatGPT-Shortcut is a prompt engineering toolkit and management library designed to organize, refine, and deploy structured instructions for large language models. It functions as a browser-based prompt injector and a self-hosted prompt database, allowing users to maintain a curated collection of specialized templates. The project features a community prompt gallery where users can publish, discover, and vote on effective templates. It distinguishes itself by integrating these libraries directly into chat interfaces via userscripts or browser extensions, enabling access to prompts through sid

GPT-Prompt-Hub is a community-driven repository and knowledge management system for structured prompt templates. It functions as a generative AI workflow tool, providing a curated library of instructions designed to guide language models through complex professional and technical tasks with consistent results. The project distinguishes itself by utilizing a version-controlled, markdown-based storage system that treats prompt engineering as a collaborative development process. By employing schema-driven metadata and standardized templating, it enforces uniform roles, objectives, and constraint

Poml is a prompt management framework and templating engine designed for authoring, versioning, and rendering structured prompts for large language models. It uses a semantic markup language to organize prompts into reusable templates, combining them with dynamic context and data to generate formatted inputs. The system distinguishes itself by decoupling core prompt logic from final presentation through a stylesheet-based approach. It provides a dedicated JSON schema output generator to enforce strict, machine-parsable model responses and a configuration interface for managing function tool s

This project is an AI agent workflow orchestrator and software development framework designed to transform high-level feature descriptions into executable implementation steps for AI assistants. It provides a structured system of prompt templates that guides large language models through the transition from product drafting to technical planning and code execution. The framework focuses on a methodology for decomposing product blueprints into sequenced lists of technical sub-tasks. It employs a system of prompt engineering to standardize outputs, ensuring that abstract requirements are conver

my-git is a comprehensive framework and reference guide for Git version control administration, repository governance, and software release management. It provides a structured approach to managing the software development lifecycle, from initial feature branching to final production deployment. The project distinguishes itself through a specialized AI-assisted development framework. This includes workflows for managing AI-generated code via automated diff reviews, intent-based commit splitting, and governance models for multi-agent coordination and session isolation using worktrees. The cod

gstack is an AI agent framework and development workflow system designed to automate the software development lifecycle. It coordinates specialized AI personas to manage tasks across product design, engineering management, and quality assurance, transforming product intent into technical specifications and final releases. The project is distinguished by its deep integration of headless browser automation and semantic code memory. It utilizes a persistent Chromium daemon for web scraping and visual auditing, and implements a searchable knowledge base that logs architectural decisions and repos

Context-Engineering is a prompt engineering framework and cognitive architecture for large language models. It provides a set of patterns and methodologies for designing structured prompts and modular reasoning flows that decompose complex tasks into specialized, step-by-step problem solving templates. The project distinguishes itself through stateful prompt management and context window optimization. It maintains persistent memory across multiple interaction turns by compressing conversation history into compact internal state cells and employs techniques to maximize information density per

This project is a collection of standardized instructions and behavioral rules designed to refine the performance of automated coding assistants. It functions as a repository of system prompts and configuration files that enforce consistent coding patterns and project guidelines within an automated development environment. The library enables modular prompt composition, allowing users to assemble task-specific instructions by merging discrete fragments into a unified execution context. By utilizing schema-based templating and declarative configuration, the project ensures that model inputs re

53AIHub is a centralized orchestration platform for deploying and managing AI agents and prompts across multiple large language model providers. It functions as a multi-model AI gateway and an operation portal for AI services, providing a unified interface to coordinate agents and prompts from various external platforms. The project distinguishes itself as a white-label AI portal designed for self-hosted infrastructure, allowing for full control over operational data on private servers or containers. It includes a comprehensive AI SaaS administration layer with a multi-tenant subscription eng

Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large language models. It functions as an automated evaluation pipeline that systematically adjusts prompt structure, constraints, and clarity to improve the accuracy and consistency of model outputs. The system distinguishes itself through a model-agnostic interface that standardizes communication across different artificial intelligence providers. It incorporates a versioned asset management system to track prompt history, enabling developers to maintain consistency and perform r

G0DM0D3 is a static web client and multi-model chat gateway designed for AI research, prompt optimization, and red teaming. It provides a unified interface to query numerous AI models in parallel, allowing for the simultaneous evaluation of different prompt variations and sampling parameters to identify the most successful outputs. The project features specialized tooling for probing safety filters and bypassing model constraints through an input perturbation engine that applies text obfuscation and character substitution. It includes a composite scoring system to rank model performance and a

SuperPrompt is an AI agent prompting tool and meta-prompting system designed to engineer complex prompts that enable autonomous behaviors and advanced reasoning in large language models. It functions as a framework for creating structured instructions and notations that guide models through multi-step tasks and autonomous workflows. The system utilizes a structured prompt library featuring XML notations and holographic metadata to force models into deeper thought patterns and novel perspectives. It employs dynamic meta-prompting to automatically rewrite operational constraints and objectives

Automatic Prompt Engineer is a framework designed to automate the generation, refinement, and performance measurement of language model instructions. It functions as a systematic tool for optimizing prompt phrasing by iteratively testing candidate instructions against specific input and output datasets to maximize task accuracy. The system distinguishes itself through an evaluation-driven approach that uses automated feedback loops to score prompt variations. By employing template-based input structuring, it ensures consistent testing environments where candidate instructions are measured aga

This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine prompts using a language model to improve output quality. It functions as a framework for generating candidate prompts and ranking their performance through correctness matching and ELO-based ratings. The system includes capabilities for model distillation, generating high-quality example pairs from frontier models to create training data for smaller models. It also provides tools to condense prompts for smaller models and transform instruction-tuned prompts into completion-b

This repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the o

Higress is an AI API gateway and cloud-native traffic manager that functions as a Kubernetes ingress controller. It provides a centralized system for routing, securing, and optimizing traffic directed toward large language models, AI agents, and microservice architectures. The project distinguishes itself through deep AI orchestration, including the ability to host and manage Model Context Protocol servers that transform REST APIs into tools for AI agents. It features specialized AI infrastructure for model request proxying, protocol translation across multiple providers, and semantic-based c

Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ

This project is an AI development workflow orchestrator and context management framework. It provides a context-aware project knowledge base and a structured prompting system designed to guide large language models through the planning, implementation, and verification phases of software development. The system optimizes AI coding contexts by using a collection of markdown files to track project state and architectural memory. It employs mode-based rule isolation and just-in-time context loading to reduce noise and ensure that only relevant rules and documentation are active for a given task.

Promptflow is a development framework and orchestrator for building applications powered by large language models. It functions as a suite of tools for designing, orchestrating, and deploying AI workflows by linking prompts, custom Python code, and language models into executable sequences. The project is distinguished by a visual AI workflow designer that allows for the creation of directed acyclic graphs of logic nodes. It provides a dedicated prompt engineering environment for versioning and comparing templates, alongside stateful execution tracing to record function calls and variable val

Click-prompt is a centralized management platform designed for engineering, organizing, and executing generative artificial intelligence prompts. It provides a dedicated workspace for users to construct, refine, and optimize prompt structures, ensuring that interactions with various artificial intelligence models remain consistent and structured. The platform distinguishes itself through a collaborative library that enables users to publish and share prompt collections, facilitating knowledge exchange within a community. It features an interactive builder that maps specific user requirements

This project provides a structured framework and toolkit for managing AI-assisted software development. It functions as an orchestration system that guides large language models through complex, multi-step coding tasks by establishing standardized methodologies for project documentation, architectural constraints, and coding conventions. The framework distinguishes itself by implementing a centralized approach to constraint enforcement and knowledge structuring. By defining global rules and curating authoritative code templates, it ensures that automated agents maintain consistency across rep

Promptify is a suite of tools designed for model evaluation, prompt management, token cost tracking, structured extraction, and unified API gateway access. It provides a standardized interface to manage requests and responses across multiple large language model providers. The project features a prompt management platform for engineering and versioning prompts with structured output validation. It includes a dedicated evaluation framework to measure model performance using precision, recall, and f1 scores against labeled datasets, alongside a token cost tracker to monitor the financial expens