🚀 Meet zhaoxuya520/reverse-skill: a gem from today's GitHub trending list.

🔗 https://github.com/zhaoxuya520/reverse-skill
📝 Reverse Engineering / Authorized Penetration Testing / Security Research Skill Router Pack AI-powered routing + On-demand toolchain bootstrapping + Self-evolving knowledge base Supports Claude Code, Kiro, Cursor, Cline, and other AI coding clients 逆向/渗透/安全技能路由包 - AI 自动路由 + 按需自举工具链 + 自动进化经验库 | 支持 Claude Code / Kiro / Cursor / Cline 等代码 AI 客户端
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The reverse-skill GitHub repository is a comprehensive cybersecurity skills router designed to navigate complex security tasks. It provides a structured approach to routing methodologies, checking available tools, and executing repeatable workflows for various scenarios, including APK analysis, binary reverse engineering, and penetration testing.

The repository includes key features such as a PRIMARY fast ladder, a task → skill routing matrix, and a local tool status index. It supports multiple platforms, including Windows, Linux, and macOS, and integrates various tools like IDA Pro, radare2, and Ghidra.

To get started, users need to clone the repository, refresh the tool index, and follow platform-specific documentation. The repository layout is well-organized, with separate sections for skills, scripts, and documentation.

The target audience includes security professionals, researchers, and AI agents looking to streamline their workflow and improve efficiency. With its MIT License and open-source dependencies, the project encourages contributions and collaboration.

In summary, reverse-skill is a powerful tool for cybersecurity professionals, providing a structured approach to complex security tasks - automate your workflow, amplify your skills.

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📌 Spotted on GitHub Trending: different-ai/openwork — let's break it down.

🔗 https://github.com/different-ai/openwork
📝 The open-source alternative to Claude Cowork (powered by opencode)
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The OpenWork project is a free, open-source desktop app designed for sharing AI workflows across various platforms, including macOS, Windows, and Linux. Its primary purpose is to provide a unified workspace where users can create, share, and reuse AI workflows, skills, and connected services with teammates or keep them private.

The key features include adding one OpenWork MCP to compatible agents like Codex, Claude Code, or Cursor, and reusing the same skills and connections across tools and machines. The desktop app offers a dedicated workspace, but it's not required, as users can access OpenWork from their existing AI agent. For larger organizations, the admin interface allows publishing capabilities, managing access, and configuring shared or per-user connections.

To get started, users can install OpenWork using a simple prompt in their AI agent or by downloading it from the official website. The OpenWork MCP brings assigned skills and plugins into any compatible agent, exposing tools like search_capabilities and execute_capability.

The project is suitable for both individual users and larger organizations, providing a flexible and scalable solution for managing AI workflows. The OpenWork Den control plane enables teams to provision inference at scale, manage access, and publish skills and plugins.

In terms of technical highlights, OpenWork uses Electron and Vite for development, and users can run multiple git worktrees at once using pnpm dev:worktree.

The target audience includes AI enthusiasts, developers, and organizations looking to streamline their AI workflow management.

In short, OpenWork empowers users to create once, share anywhere, making AI workflow management more efficient and accessible. The ability to reuse and share workflows seamlessly across different platforms and tools is a game-changer, and that's what makes OpenWork a standout solution - workflows just got a whole lot more open.

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⚡ microsoft/generative-ai-for-beginners is making waves. Here's the full picture.

🔗 https://github.com/microsoft/generative-ai-for-beginners
📝 21 Lessons, Get Started Building with Generative AI
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The Microsoft Generative AI for Beginners GitHub repository offers a comprehensive 21-lesson course on building Generative AI applications. The course covers the fundamentals of Generative AI, including Large Language Models (LLMs), Prompt Engineering, and responsible AI development.

Key features of the course include:
- Python and TypeScript code samples
- Support for Azure OpenAI Service, Microsoft Foundry Models, OpenAI API, and Foundry Local
- A Course Setup lesson to help with development environment setup

To get started, you'll need basic knowledge of Python or TypeScript and a GitHub account. The course is suitable for beginners and intermediate learners.

Takeaway: With this course, you can learn the fundamentals of Generative AI and start building your own applications in no time.

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🧠 Channel: https://xn--r1a.website/GithubRe

🌟 Panniantong/Agent-Reach caught my eye on GitHub Trending today.

🔗 https://github.com/Panniantong/Agent-Reach
📝 Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
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Agent Reach 是一个帮助 AI Agent 获取互联网能力的工具。它让 Agent 能够读取网页、搜索推特、观看 YouTube 视频、刷小红书等，不需要任何配置。只需复制一句话给你的 AI Agent，就能安装 Agent Reach 并解锁这些功能。

主要特点：
- 完全免费，所有工具开源，所有 API 免费
- 隐私安全，Cookie 只存在本地，不上传不外传
- 持续换代，每个平台都是「首选 + 备选」多后端路由
- 兼容所有 Agent，任何能跑命令行的 Agent 都能用

技术亮点：
- 依赖 pip install 装好 agent-reach 命令行
- 自动配置搜索引擎，通过 MCP 接入 Exa（免费，无需 API Key）
- 支持多种平台，包括网页、YouTube、Twitter、Reddit、Facebook、Instagram 等

使用方法：
- 复制这句话给你的 AI Agent：帮我安装 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md
- 更新也是一句话：帮我更新 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/update.md

目标受众：
- AI 爱好者
- 开发者
- 企业用户

总结：Agent Reach 是一个让 AI Agent 能够获取互联网能力的工具，简单、免费、安全、兼容所有 Agent。安装 Agent Reach，一键解锁互联网能力。

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🧠 Channel: https://xn--r1a.website/GithubRe

🔍 Deep-diving into TencentCloud/TencentDB-Agent-Memory — fresh off the trending list.

🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a powerful tool designed to enhance the memory capabilities of AI agents, allowing them to learn from workflows, retain task context, and reuse past experiences. At its core, it features symbolic short-term memory and layered long-term memory, which work together to ensure that agents not only remember more but also reason better.

The symbolic short-term memory offloads heavy tool logs into compact Mermaid symbols, reducing token usage and improving task success. Meanwhile, the layered long-term memory distills fragmented conversations into structured personas and scenes, instead of relying on flat vector piles.

When integrated with OpenClaw, it can cut token usage by up to 61.38% and improve the pass rate by 51.52%. The system is designed with heterogeneous storage and progressive disclosure in mind, allowing for a dual-layer storage strategy that balances robust full-text retrieval with high information density and white-box inspection.

To get started, users can follow the Quick Start guide, which provides steps for installing the plugin, enabling it, and configuring it for use with OpenClaw or Hermes. The plugin is suitable for developers and researchers looking to enhance the memory capabilities of their AI agents.

In a nutshell, TencentDB Agent Memory is all about empowering agents to remember what should be remembered, so people can focus on judgment, creation, and work that truly matters - let the machines handle the memorization, and humans handle the innovation.

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🧠 Channel: https://xn--r1a.website/GithubRe