Provides a manifest-driven marketplace of official Cursor plugins for developer workflows and agent integrations. Each plugin lives in its own directory with a .cursor-plugin manifest; examples include agent skills, PR review canvases, SDK integrations, CI/team tooling, and orchestration for parallel agent work.
Fetches multi-source content (webpages, YouTube, PDFs, WeChat, paywalled articles, podcasts), uploads it to Google NotebookLM, and generates outputs such as podcasts, PPTs, mind maps, or quizzes. Differentiators: automatic paywall-bypass pipeline, Claude Code Skill integration, and CLI + MCP components for WeChat and document scraping.
Provides a REST server that lets AI agents browse sites while avoiding common bot-detection by running Camoufox (a Firefox fork with C++-level fingerprint spoofing). Returns compact accessibility snapshots, stable element refs, session isolation, proxy/geoIP support, and agent-friendly endpoints (click, type, snapshot, transcripts).
Local-first AI desktop app that combines multi-model chat and a proactive Agent workspace to embed agent workflows into daily work. Features per-workspace Skills, MCP support, Feishu/remote-robot bridges, and local JSON/JSONL storage for privacy and portability.
Centralized operations dashboard for OpenClaw agent fleets — orchestrate boards and tasks, manage agent lifecycles, enforce approval-driven governance, and operate gateway-connected runtimes from a single UI and API.
Equips AI coding agents with reusable AWS skills (deployment, serverless, Amplify, SageMaker) by packaging agent skills, MCP servers, hooks, and references so agents invoke vetted workflows instead of bloating prompts.
Turns natural-language directions into end-to-end video editing workflows: LLM-powered planning, media search/organization, ASR rough-cut, and reusable Style Skills for consistent storytelling. Integrates agent Skills (OpenClaw/Claude Code) and optional AIGC transitions.
A TypeScript framework for building programmable, headless autonomous agents with a harness-centric runtime. Includes an SDK and CLI, virtual sandboxes (just-bash) with optional full container sandboxes, provider-agnostic model settings, and connectors for CI/Daytona/MCP—suited for deployable agent runtimes.
Local integration layer that lets AI agents discover and securely call OpenAPI, MCP, GraphQL, or custom JavaScript functions. Centralizes a shared tool catalog, auth, and policy surface across multiple agents, with a local web UI and CLI for runtime control.
Provides a local MCP server that returns precise, symbol-level code (functions, classes, imports) via tree-sitter parsing so AI agents send only the bytes they need—commonly cutting code-reading token usage 95%+ and enabling compact packed responses for further savings.
Local LLM inference server for Apple Silicon that exposes an OpenAI-compatible API and a macOS menubar app. Uses continuous batching and a two-tier KV cache (RAM + SSD in safetensors) to persist context across restarts, enabling practical multi-model serving and fast local coding workflows.
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.