This repository implements a full-stack content operations platform exclusively for Xiaohongshu (XHS), the Chinese social media platform. It provides a complete lifecycle for managing XHS notes: discovering content via search or URL crawling, saving to a local library, AI-assisted rewriting and title/tag generation, scheduling and publishing through XHS's Creator API, and monitoring engagement metrics. The system is built as a modern web application with a FastAPI backend (Python) and a React frontend (TypeScript, Ant Design), using SQLAlchemy for persistence and APScheduler for background job execution. The architecture is a monolith with clear separation of concerns: backend API routers for accounts, notes, drafts, AI, tasks, publishing, monitoring, and analytics; a service layer for business logic; and a set of adapters that encapsulate the reverse-engineered XHS API clients. The frontend provides a dashboard, discovery page, content library, draft editor, publish center, auto-ops scheduler, and analytics views. Authentication uses JWT tokens with refresh logic, and the system supports multiple XHS account types (PC web and Creator) via QR code or phone login. A notable aspect is the extensive reverse-engineering of XHS's anti-bot mechanisms. The codebase includes custom JavaScript modules for generating signatures (b1, MNS, X-S, X-T, websectiga, profileData) and browser fingerprint emulation, all executed locally via Node.js subprocesses. This allows the platform to interact with XHS APIs without a real browser, enabling automated content operations at scale. While the primary focus is on XHS, the platform abstraction layer (PlatformId enum, adapter interfaces) suggests an intention to support other social media platforms, though only XHS is currently implemented. The repository is a hybrid of application and data engineering: it is primarily a production-grade web application with scheduled tasks, user management, and a rich UI, but it also contains data pipeline elements such as crawling, normalization, and storage of XHS content for later analysis. The data models and analytics endpoints provide research-like capabilities (keyword tracking, engagement trends, benchmarks), but these are embedded within the application rather than forming a standalone data research project.