Reusable skills—instructions plus helper scripts—that extend AI coding agents. Covers Vercel deployment audits, React performance rules, UI accessibility checks, and writing guidelines; each loads only when a relevant task appears.
Researches the last 30 days of public discussion across Reddit, X, Bluesky, YouTube, TikTok, Instagram, HN and Polymarket, then synthesizes a citation-rich briefing and copy-paste prompts. Multi-source scoring, comparative mode, and optional watchlist; requires API/auth for some sources.
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.
Runs background coding agents in isolated sandboxes to autonomously handle development tasks, create pull requests, and integrate with Slack, GitHub, Linear and webhooks. Supports multiplayer sessions, multiple LLM providers, fast startup via snapshots and prebuilt images; designed for single-tenant deployments.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.
Models an AI agent's context as a file system, unifying memory, resources, and skills instead of flat vector RAG. Uses L0/L1/L2 tiered loading to cut tokens, directory-recursive plus semantic retrieval, and visualized retrieval traces for debugging.
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.
Provides a catalog of NVIDIA-verified, portable “skills” — instruction sets that teach AI agents how to use NVIDIA libraries, models and platform tools. Each skill is published with detached signatures and evaluation artifacts for verifiable reuse in agent workflows.
Generates protocol-bound GEP prompts that guide iterative evolution of AI agent behavior from runtime logs, producing auditable EvolutionEvents and reusable Genes/Capsules. Node.js-based and works offline; optional EvoMap network integration enables skill sharing, worker pools and leaderboards while git provides rollback and validation.
Self-hosted personal AI agent runtime that runs chats, tools, automations and long-term memory for persistent workflows. Small, readable core with a bundled WebUI, multi-chat integrations, an OpenAI-compatible API and a Python SDK for easy extension and deployment.
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.
Provides a workflow layer for OpenAI Codex CLI to bootstrap stronger Codex sessions, add reusable agent roles/skills, and manage durable project state under .omx. Includes team runtime, canonical skills, and monitoring surfaces.