This skill is based on practical Power Platform delivery patterns for safely creating, reviewing, and modifying Power Automate cloud-flow JSON in Dataverse solution exports. It focuses on issues that often break real client automations: runAfter paths, renamed action references, connection references, hardcoded values, TRY/CATCH logging, import safety, validation, and rollback notes.
Scan AI Skills and MCP servers before installation. Detect risky scripts, permissions, dependencies, network access, credential exposure, file changes, and suspicious configurations — then generate a clear evidence-based security report.
An interactive Claude Skill that generates a customized, security-reviewed Terraform repository for Amazon Bedrock AgentCore, IAM least-privilege policies, Knowledge Base + vector store, Gateway tool registration, and Runtime deployment, scoped to your actual agent instead of a generic template dump.
在你自己的電腦組建一支 AI 團隊：一位主管 AI 拆解任務、多名員工 AI 平行施工、逐檔驗收成果；附即時網頁監控台、一鍵啟動器與零基礎說明書（Windows/macOS），下載安裝即用。
此Skill整合了APP製作的角色權限控管、智慧財務自動結算、動態資源預約排程、系統級通知功能、分工遊戲化模組等等 本 Agent 專長於： 執行階層化權限管理（EX：房長/財務/成員）。 自動化記帳與私密催繳通知。 即時資源調度與過期自動清理。 提供溫馨、簡潔的 AI 生活助理服務。 無論是共居空間、工作室或宿舍，透過 CollabSpaceOS 皆可快速部署具備高安全性與生活溫度感的專屬 App。 匯入此技能包，讓您的 AI 瞬間進化為精通 React + Firebase 的資深架構師！本 Skill 內建『蒐集代幣的遊戲化任務』、『智慧財務記帳』與『PWA 系統推播』等高度模組化的領域知識。 您的專屬 AI 將為您精準產出高品質代碼，完美處理『跨時區同步』與『防資料流失』等高階邏輯。從架構整合、客製化微調到進階 Debug，為接案者與開發者省下破百小時的摸索期。獲取神級框架，極速部署您的專案！」
Your All-in-One AI for Online Business. Create digital products, content, ADS, Emails, Sales Pages and strategies to build and grow your business - whether you’re staring from zero or already selling online.
Meta-Skill Architect is a systems-engineering framework that transforms ideas into production-ready AI skills. It combines prompt engineering, software architecture, quality assurance, knowledge management, and validation workflows to create scalable, reusable, marketplace-ready skill packages with high reliability, traceability, and triggering accuracy.
We built Autowin by putting models in a loop: discover upgrades → test them → judge them with an assembly of adversarial sub-agents — and we ran that very process on itself, iterating the kit until each piece held up under its own review. The result: from one loosely-defined need, you get back work that has been built, tested, and scored by a panel of judge sub-agents — with closure authority kept outside the model (deterministic checks + you), so an "it's done" is never just taken on faith.
