170 boosters for "codex" — open source, verified from GitHub, ready to install
Lint agent configurations before they break your workflow. Validates Skills, Hooks, MCP, Memory, Plugins across Claude Code, Cursor, GitHub Copilot, and Codex CLI. Invoke when user asks to: If not found, install:
在 Cursor 中使用 Super Dev 进行规范驱动开发。 1. 规范先行 - 在编码前阅读并理解规范 2. 遵循关键词 - 严格遵守 SHALL/MUST/SHOULD/MAY
A testing practices guide for Deno projects that helps developers write, organize, and run tests effectively using Deno's built-in testing framework. Useful for teams adopting standardized testing workflows in Claude Code environments.
"name": "qt-development-skills", "description": "Agentic engineering skills for Qt software development, including Qt C++/QML code review, QML coding, and Qt C++/QML code documentation. Bundles the hosted Qt Documentation MCP server for live Qt API lookups. These skills use AI and can make mistakes.
1. Gather requirements: Ask user for project name, program functionality, required instructions, and frontend features 2. Generate project structure: Create complete directory tree following Anchor conventions 3. Create Rust smart contract: Generate lib.rs, state.rs, errors.rs, and instruction handl
Generate images using ModelScope's Tongyi-MAI/Z-Image-Turbo model. Optional: Set to use your own API key.
Automate reproducible marketing screenshot capture for a SwiftUI iOS app across multiple locales, with two parallel output streams: 1. Full-screen captures — every marketing-relevant screen, with deterministic seeded data, real status bar / safe-area chrome 2. Element captures — isolated renders of
"version": "2.30.0", "description": "The missing DevOps layer for coding agents. Flow, feedback, and memory that compounds between sessions.", "name": "Boden Fuller",
You are managing the TLive IM Bridge — bidirectional chat with AI coding tools from Telegram, Discord, or Feishu. The Bridge uses the Claude Agent SDK (or Codex SDK) to interact with the AI coding tool. It is completely independent from the optional Go Core web terminal server. Before any command ex
Initialize standardized project memory: Core idea: bottom feeds top, top constrains bottom. Logs and TODOs are raw facts. Handoff marks the next resume point. Wiki is the current snapshot. Structure manages file organization. Docs index manages document output. Update Log records versioned milestone
Matchms is a Python library for mass spectrometry data processing, enabling researchers to import spectra from multiple formats, standardize metadata, calculate spectral similarities, and perform metabolomics analysis within Claude Code environments.
Provides complete specifications and role templates for 37 specialized agent types in a dynamic multi-agent system, enabling developers to build scalable, parallel AI workflows. Useful for teams building complex applications requiring orchestrated AI agents with clear capability boundaries.
FastAPI Templates provides production-ready project structures with async patterns, dependency injection, and error handling for developers building high-performance APIs. Backend engineers and Python developers benefit from standardized FastAPI scaffolding that accelerates project setup.
Smart Coding MCP enhances developer productivity with AI-powered semantic code search, intelligent indexing, and hybrid search capabilities integrated into Cursor, Claude Desktop, and VS Code. It's designed for developers working with local LLMs who need faster, smarter code discovery and navigation.
"name": "brand-docs", "description": "AI on-brand document generator for Claude Code & Codex: turn a company's Word, PowerPoint or Excel template into unlimited on-brand .docx/.pptx/.xlsx. Extracts a reusable Brand Profile once - off-brand output impossible by construction. Skills: brand-docx, brand
A structured code review workflow that orchestrates specialized AI agents to perform quality, security, performance, and documentation reviews within a 4-hour timeline. Useful for development teams seeking systematic, multi-perspective code quality assurance.
Coordinate heterogeneous AI teams: one creates, two review from different angles. Uses Claude Code's native Agent Teams capability with Codex and Gemini as reviewers. Different AI models have fundamentally different review tendencies. They don't just find different bugs — they look at completely dif
Zettelkasten memory for AI agents. Markdown cards in with . npm package . Distributed as CLI + MCP server + Claude Code plugin + VS Code extension + Pi extension. Build has a known TS error ( optional dep). Ignore it — it compiles fine for distribution. If you add a new MCP tool:
This is the Go implementation of Vibes (main branch). The Python version lives on the branch.
"description": "Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage",
This is not report writing—it is persuasion. In every scenario you do the same thing: All briefing types share one backbone; section emphasis differs. Each type is an instance of the 5-part framework.
你是 OpenClaw 内部负责操作 Codex CLI 的执行器。你的职责不是“给用户解释 Codex 是什么”，而是基于当前仓库提供的 runtime 层，把 Codex 任务稳定启动、持续跟踪、必要时接管和汇报。 可以借鉴 的 runtime/session 思路，但不照搬 Claude 专用逻辑。 优先直接使用 Codex review，而不是自己拼一套“审查 prompt 模拟 review”：
"name": "symfony-ux-skills", "description": "AI agent skills for the Symfony UX frontend stack: Stimulus, Turbo, TwigComponent, LiveComponent, Icons, and Map.", "name": "Simon Andre",
