Desktop-first agent client that composes LLM-driven agents into document-centric, multi-session workflows; it wires APIs, MCPs and local tools into shareable sessions, supports multiple LLM providers, and exposes a headless server + CLI for automation.
A curated, security-first registry of verified, tested skills you can install into AI coding agents. Each skill is human-curated, scanned (Snyk/static analysis), and integrity-locked; delivered via a CLI and optional MCP server to multiple agents (Claude Code, Cursor, Copilot, etc.).
Desktop + CLI agent-native client for managing multi-session conversations, connecting to multiple LLM providers and external data sources, and creating shareable agent skills and automations without editing code.
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.
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).
A concise, four‑principle guideline (as CLAUDE.md or a Claude Code plugin) that teaches LLMs to: state assumptions, prefer simple solutions, make surgical edits, and use testable success criteria — reducing overcomplication and unwanted changes when an LLM edits code.
Improves Claude Code's coding behavior with a single CLAUDE.md that prescribes four practical rules—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—to reduce LLM assumptions, overengineering, and unrelated edits.
Native macOS terminal that surfaces and manages AI coding-agent sessions with vertical tabs, pane-level notification rings, a scriptable in-app browser, and a CLI/socket API — Ghostty-compatible and built in Swift/AppKit for low memory and fast startup.
Browser dashboard for OpenClaw Gateways that shows agents, streams runtime events, supports chat and exec approvals, and lets you configure jobs. Runs a small server process (Node + SQLite) and supports local or cloud setups with Tailscale or SSH access.