The ai-boost/awesome-prompts repository is a curated collection of prompt templates designed to configure AI agents for specific roles and tasks. It contains hundreds of specialized prompts for agent architectures (e.g., personal agent brains, multi-agent orchestrators), agent skills (e.g., coding agents, security auditors), and domain-specific agents (e.g., healthcare operations, legal analysts). Each prompt encapsulates non-obvious design patterns for agent systems, such as three-layer retrieval for memory, plan-execute loops, declarative UIs, and skill-based architectures. The repository serves as a reference for prompt engineering and agent design, enabling users to build reliable, safe, and effective LLM-based agents without starting from scratch.
Kiln is a full-stack AI development platform for building, evaluating, and optimizing LLM-based tasks and agents. It provides a desktop application (Tkinter tray + FastAPI server) with a Svelte web UI, plus a core Python library and a cloud server component. The system supports the complete AI workflow: project/task management, multi-provider LLM orchestration via LiteLLM, evaluation with multiple judge types (LLM-as-judge, code eval, exact match, pattern match, etc.), synthetic data generation, RAG pipelines (chunking, embedding, vector stores), fine-tuning, prompt optimization, and Git-based project versioning. It also includes a 'Kiln Pro' cloud service for advanced features like copilot-assisted spec refinement and batch generation. The architecture is a monorepo with uv workspace, featuring extensive test coverage (unit, integration, e2e) and CI/CD pipelines. While the primary character is an application, it incorporates significant data engineering patterns for evaluation pipelines, synthetic data generation, and statistical analysis.
This is a production-grade, AI-powered code review automation tool that integrates with multiple version control systems (GitHub, GitLab, Bitbucket Cloud/Server, Azure DevOps, Gitea) and multiple LLM providers (OpenAI, Claude, Gemini, Ollama, Bedrock, OpenRouter, Azure OpenAI). The system is implemented as a Python async CLI application, packaged as a Docker image, and includes CI/CD integration templates for GitHub Actions, GitLab CI, Jenkins, Bitbucket Pipelines, and Azure Pipelines. Architecturally, it follows a clean layered design: a CLI layer (Typer) drives commands; a configuration layer (Pydantic Settings with multiple file/env sources) manages nested settings for LLM, VCS, agent, review, artifacts, and logging; a service layer orchestrates the review process through runners, gateways, and internal services (prompt building, diff analysis, policy enforcement, cost tracking, artifact persistence); and a client layer provides unified abstractions for VCS and LLM providers with provider-specific implementations. The tool supports multiple review modes: inline comments on specific code lines, summary comments on the entire PR, context review (full repository exploration via an optional agent loop), and reply to existing comments. The agent loop uses a ReAct pattern with tool execution (shell commands like `rg`, `cat`, `git`) to gather deeper context, with duplicate detection and context limits. A dry-run mode allows testing without posting to VCS. The system also includes a hook service for lifecycle events, cost tracking per request, and optional artifact saving of LLM interactions. Key strengths include extensive test coverage (unit tests with fake clients and fixtures), generic reusable components (HTTP client with retry, pagination helpers, diff parser, LLM output JSON parser, cost service), and a well-structured configuration system that supports discriminated unions for provider-specific settings. The codebase is actively maintained, uses modern Python async patterns, and demonstrates high craftsmanship in its abstraction layers and separation of concerns.
This repository, named 'AI Short' (ChatGPT-Shortcut), is a production-grade Docusaurus-based web application that serves as a curated, multilingual library of over 5,000 AI prompts for ChatGPT, Gemini, Claude, and similar tools. The application provides a searchable, tag-filterable interface with prompt cards, copy-with-tracking, and detailed pages featuring FAQs and related prompts. It supports 18 languages via separate Docusaurus builds and includes a browser extension (Chrome/Firefox) for quick access. Beyond the static prompt data, it offers a community platform with user accounts (OAuth), favorites, custom prompt creation, voting, comments, and a level system. The frontend is a React 19 SPA with Ant Design 6, featuring client-side caching, optimistic UI updates, and lazy-loaded components. The backend (Strapi) is not open-sourced but the API contracts are fully defined. Deployment options include standard (shared backend), offline (localStorage), and full self-hosted setups. The code demonstrates strong engineering practices: clean component decomposition, custom hooks for data management, a two-tier cache system with ETag support, and thoughtful accessibility patterns. The prompt data itself is static JSON, generated from a master file via a Python pipeline, and includes rich metadata for SEO and cross-referencing.
Powerlevel10k is a high-performance Zsh prompt theme that provides extensive customization through a configuration wizard and pre-built style templates. The system consists of a core Zsh script (`internal/p10k.zsh`) that handles prompt rendering, segment definitions, caching, and async background computation via a worker process. It includes a bundled C++ daemon (`gitstatusd`) for extremely fast git status queries, which communicates with the shell through a custom binary protocol. The architecture is a monolith within the Zsh ecosystem, with the prompt engine tightly integrated into the shell's widget system. Key entry points are the theme file (`powerlevel10k.zsh-theme`), the `p10k` command (for configuration, reload, and segment help), and the `gitstatusd` binary. External dependencies include Zsh ≥5.1, git, and optional tools for various prompt segments (e.g., kubectl, terraform, pyenv). The repository is explicitly in limited maintenance mode with no new features and most bugs unfixed, but the codebase is mature and well-optimized.
Nacos is a production-grade dynamic service discovery, configuration management, and service management platform, originally built by Alibaba. The codebase is a large Java/Spring Boot monolith with a React frontend, organized into modules for core infrastructure (cluster, gRPC, Raft consensus, Distro sync), configuration management (CRUD, long-polling, gray rules, history), naming/service discovery (instance registration, health checks, push v2), and an extensive plugin system (auth, datasource, encryption, control, visibility). In recent versions, Nacos has expanded into AI resource management, adding modules for MCP server registry, agent specs, skills, prompts, A2A agent discovery, vector search indexing, and pipeline execution. The repository includes two frontend codebases (legacy console-ui and modern console-ui-next with Radix UI/Tailwind), a Java client SDK, and comprehensive unit/integration tests. The architecture is modular with SPI-based plugin loading, event-driven internal communication, and support for multiple databases (Derby, MySQL, PostgreSQL, Oracle). Despite its size and rapid AI feature growth, the codebase maintains solid engineering practices with clear separation of concerns, extensive test coverage, and a well-defined plugin contract.
Liquid Prompt is a feature-rich, adaptive shell prompt for Bash and Zsh. It dynamically displays contextual information such as VCS status (Git, Mercurial, Subversion, Fossil, Bazaar), system load, battery level, temperature, Wi-Fi signal, and more. The prompt is highly configurable via environment variables, config files, themes, and presets. The architecture is a monolithic shell script (~5000 lines) with a theme engine that allows custom prompt layouts. It includes a comprehensive test suite using shunit2, CI across 20+ shell versions, and extensive documentation. The codebase is actively maintained with regular releases and deprecation paths for older features.
53AIHub is an open-source enterprise AI knowledge management and agent orchestration platform. The codebase reveals a multi-tenant SaaS/self-hosted system that allows organizations to build, deploy, and operate AI agents across multiple backends (OpenAI, Coze, Dify, Gemini, etc.), manage knowledge bases with full RAG pipelines (document parsing, chunking, embedding, graph extraction, wiki generation), record and transcribe meetings with insight extraction, and provide a unified search and chat interface. The architecture is a Go monolith backend using Gin and GORM, with a monorepo frontend comprising three React apps (admin console, end-user portal, and embeddable agent plugin) sharing a common business logic and component library. Data flows center on document ingestion pipelines, real-time agent relay with tool execution, and recording/speech processing. The system integrates with vector stores (Qdrant), Elasticsearch, Redis, and various LLM providers. While primarily an application, it contains substantial data processing pipelines characteristic of a data_research project, particularly the RAG and recording insight modules.
This repository is a curated, multi-vendor collection of leaked system prompts from major AI chatbots (ChatGPT, Claude, Gemini, Grok, Perplexity, DeepSeek, etc.), captured verbatim from production systems. It serves as a reference corpus for studying AI behavior instructions, safety guardrails, tool definitions, and product positioning across model versions and vendors. The repository also includes Claude Code bundled skills (code review, design-sync, dataviz, deep research) and Claude Design starter components, which are supplementary tooling. While primarily a documentation archive, it contains some well-crafted reusable components such as color palette validation scripts, a design system build/diff pipeline, and a deep research workflow script. The prompts are organized by vendor with version tracking, and the repository is actively maintained with recent updates. However, the tooling scripts lack dependency pinning, and the prompts are time-sensitive snapshots that will drift as products evolve.
Prompt Optimizer is a multi-platform AI prompt optimization tool that helps users craft better prompts for large language models (LLMs) and image generation models. The system supports web (Vue 3 + Naive UI), desktop (Electron), Chrome extension, MCP server, and Docker deployment via a pnpm monorepo. Core workflows include system/user prompt optimization, evaluation with structured compare (pair-judge, synthesis), image generation (text2image, image2image, multi-image), variable extraction/value generation, a template system with 40+ built-in bilingual (zh/en) templates, and smart favorites with resource-aware asset management. The architecture cleanly separates framework-agnostic core services (LLM adapters, evaluation, storage, template processing) from UI composables and Electron proxies, using adapter, registry, and factory patterns extensively. A notable strength is the comprehensive test infrastructure spanning unit (Vitest), integration (real LLM API helpers gated by environment flags), and E2E (Playwright with VCR record/replay). Weaknesses include significant template duplication (bilingual files maintained separately), IPC fragility between Electron main/renderer, provider adapter maintenance overhead (16+ text adapters, 6+ image adapters), and some dead/deprecated code paths carrying legacy migration layers.
POML (Prompt Orchestration Markup Language) is a developer toolchain for structured, reusable LLM prompt engineering. It provides an HTML-like markup language with semantic components, CSS-like styling, templating (variables, loops, conditionals), and multimodal data integration (PDFs, images, tables). The repository contains a core TypeScript/React SDK, a Python wrapper with OpenAI/LangChain/MLflow integrations, a VS Code extension (LSP, live preview, test command), and a Chrome browser extension for content extraction. It is packaged as CLI tools and libraries on npm and PyPI. Designed for building maintainable prompts in LLM applications, it also includes research artifacts (arXiv papers) and a tracing system for debugging prompt execution.
CL4R1T4S is a repository that functions as a curated archive of extracted system prompts, tool definitions, and behavioral instructions from major AI models and agent platforms. It contains no executable code, data pipelines, or analytical scripts. The entire repository consists of flat text and markdown files organized by vendor (e.g., OPENAI, ANTHROPIC, XAI, CURSOR, REPLIT), each containing what are claimed to be verbatim system prompts for models like ChatGPT, Claude, Grok, Gemini, and various coding agents like Devin, Cursor, Windsurf, and Replit. The repository's stated purpose is transparency — making visible the hidden instructions that shape AI behavior. However, from a data engineering or software architecture perspective, there is no data lineage to trace, no transformation logic to evaluate, and no code to assess for craftsmanship. The files are static documents with no programmatic relationship between them. There are no tests, no dependency manifests, no environment definitions, and no data processing logic of any kind. The collection has some research value as a comparative corpus for prompt analysis, but it is entirely unstructured as data. There are no schemas, no metadata standards, no versioning discipline, and no way to verify the authenticity or currency of the extracted prompts. The README itself contains signs of prompt injection attempts and unprofessional editorializing that undermine the project's credibility as a serious transparency effort. There are no reusable components, no engineering patterns, and no data operations present.