Agent Reach is a Python CLI tool that serves as a capability layer for AI agents, giving them one-click access to ~15 internet platforms (Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu, LinkedIn, etc.) without requiring manual setup. It does not wrap these platforms itself; instead, it selects, installs, health-checks, and routes to the most reliable upstream CLI tools (e.g., twitter-cli, yt-dlp, gh CLI, OpenCLI, mcporter) for each platform. The architecture is built around a multi-backend channel pattern: each platform has an ordered list of backends (primary + fallbacks), and the `doctor` command probes each candidate in order to determine the active backend. This design handles upstream tool instability gracefully by allowing fallback routing without code changes. The codebase demonstrates strong engineering practices: a clean Channel ABC with `check()` and `can_handle()` contracts, a robust probe system that distinguishes missing/broken/timeout/error by actual execution, security-hardened file operations (atomic private writes, symlink rejection, bounded reads), and SSRF protection in URL normalization. The CLI provides commands for installation (safe-by-default, read-only mode), diagnostics, configuration, and skill registration for agent environments (Claude Code, OpenClaw, Cursor, OpenCode). An optional MCP server exposes read-only status. Agent Reach is purely an application — there are no notebooks, data pipelines, or ML components. Its value lies in reducing the friction of giving agents internet access, with a focus on zero-config defaults for public platforms and cookie-based auth for login-required ones. The project is actively maintained, with documented responses to upstream API changes (e.g., Bilibili blocking yt-dlp, Twitter API pay-per-use).
chubbyskills is a personal knowledge management CLI toolkit that ingests content from over a dozen platforms (YouTube, Bilibili, X, Xiaohongshu, WeChat, Douyin, Podcasts, etc.), transcribes audio/video locally or via cloud providers, and outputs structured Markdown notes with YAML frontmatter (schema v1). The system is architected as a collection of modular "skills" (one per platform) orchestrated by a unified CLI (`tools/chubby.py`), with shared utilities in `chubby_common/`. It includes an incremental knowledge base indexer using SQLite FTS5 and optional semantic embeddings, an MCP server exposing six tools for AI agent integration, and a deterministic evidence brief generator with integrity validation. Operation is local-first: transcription uses SenseVoice and faster-whisper, video downloading uses yt-dlp, and all processing is designed to work offline. Cloud transcription (Atlas/MuAPI) and LLM enrichment (DeepSeek) are optional extensions. The repository also provides a skill installer, platform smoke testing, output validation, and retry mechanisms with credential redaction. Despite the breadth, the codebase maintains clean separation of concerns, with the shared library standing out as a reusable zero-dependency core.
Auto-Redbook-Skills is a CLI tool for creating and publishing Xiaohongshu (Little Red Book) social media notes. It takes Markdown content with YAML front matter, renders it into a cover image and multiple card images using Playwright (headless browser), and optionally publishes the note to Xiaohongshu via a built-in, reverse-engineered HTTP signature client. The tool supports 8 visual themes (e.g., sketch, neo-brutalism, botanical) and 4 pagination modes (manual separator, auto-fit, auto-split, dynamic height). The publishing pipeline is entirely self-contained within the repository, using a vendor module that implements the Xiaohongshu creator platform's authentication, signing, and upload protocols without external API services. The project is primarily a content creation automation tool, not a data research project, though it does involve some data transformation (Markdown to images).
The repository is an MCP (Model Context Protocol) server designed to interact with Xiaohongshu (Little Red Book) social media platform. It exposes a set of tools via the MCP standard that enable AI assistants to perform actions like checking cookie validity, retrieving home feed, searching notes, fetching note content and comments, and posting comments. The server requires a valid Xiaohongshu cookie obtained from web login for authentication. Architecturally, the application follows a modular design separating the MCP transport layer (implemented using FastMCP in `xhs_mcp/__main__.py`) from the business logic handled by API clients. Two API implementations exist: the original (`xhs_mcp/api/xhs_api.py`) uses curl_cffi and execjs to reverse-engineer and execute JavaScript for generating required anti-bot signatures (X-S, X-T headers), while the v2 version (`xhs_mcp/api/xhs_api_v2.py`) employs Playwright for JavaScript execution in a browser context. The MCP server dynamically registers tools from these API modules, manages server lifecycle with proper resource cleanup, and handles authentication errors by prompting cookie validation. The project demonstrates solid engineering practices with clear separation of concerns, async/await patterns for non-blocking I/O, and environment-based configuration for sensitive data. However, it faces inherent technical drift due to reliance on Xiaohongshu's private undocumented APIs, which are subject to frequent changes that break signature generation mechanisms. The dual API implementations indicate ongoing efforts to adapt to such changes, though they introduce maintenance overhead. Dependency management is modern using uv and Python 3.12, but version constraints (e.g., curl-cffi<0.14.0) may pose future compatibility risks.
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.