Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.
An AI personality collaboration tool based on Model Context Protocol (MCP) that enables users to summon and collaborate with multiple AI personas for intelligent analysis and problem-solving.
Enables production lines to self-service register data sources and tools, and downstream AI agents to discover and invoke them through the standard MCP protocol.
Provides AI agents with low-latency reflex tools for fast safety checks, option selection, verification, and scoring using calibrated probabilities, reducing reliance on slow LLM deliberation.
Enables orchestration of multiple Claude Code sessions for complex multi-task coding projects with HEAD/SUB coordination and dependency-based task execution.
An MCP server that decides whether each step of an agent requires cheap intuition (System 1) or expensive deliberation (System 2) by learning from experience rather than hand-written rules.
X adapter for SurfAgent that gives AI agents X-native verbs for navigation, extraction, posting, replies, likes, reposts, proof-first task execution, recovery, and deeper research workflows.
Provides deterministic intent routing and safety gating for multi-agent systems, enforcing P0 critical word detection, mutually exclusive hard rules (MX-1/2/3), and a permission matrix as pure functions via four MCP tools.
An MCP server that allows Claude to use OpenAI's image generation capabilities (gpt-image-1) to create image assets for users, which is particularly useful for game and web development projects.
Enables personal AI agents to perceive ambient context, retain episodic memory with decay, act proactively, and route routine decisions through fast System-1 reflexes while escalating ambiguous multi-hop plans to frontier LLMs. Includes reversible execution checkpoints, token budgets, and local-first memory retention, and plugs into MCP clients like Claude Desktop, Cursor, and Windsurf.
Enables personal AI agents to sense ambient attention cues, compute intent probabilities, and execute reversible guarded dry-run actions without prompting. It routes routine reflexes through a fast System-1 decision layer backed by decaying episodic memory and strict safety and privacy guardrails.
Enables AI agents to manage Kanban boards, cards, sessions, and automations through a unified MCP interface with real-time updates, supporting delegation, agent sessions, and human-in-the-loop interactions.