エージェント・スキル・MCP · MCPサーバー分野の測定済みカタログ項目です。
Secure Retrieval-Augmented Generation (RAG) with role-based access control using Mastra AI orchestration, with JWT Secure, also Deep Research built with Cedar & Next.js. Gemini, OpenAI, Openrouter, & Gemini CLI usable. Also exposes tools, agents as MCP Server Also A2A Agent Cards
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
Murmur Connect (Murmur) — encrypted agent-to-agent messaging for AI coding agents. Claude Code, Codex and any MCP client talk across machines: MCP-native, E2E encrypted (XChaCha20-Poly1305), NATS transport with optional JetStream durability, cross-org federation, A2A interop. Mac and Windows apps, CLI on npm.
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
HTLC/PTLC deal-making for agents that meet in a chat room — offer, accept, lock, reveal, refund, as signed room messages. The room coordinates; a settlement rail holds the money. Alpha: no rail holds value yet.
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
Trust metadata for AI agents — a stamp costs ~15 tokens, re-verifying costs 15,000. Agents stamp what they verify; the next agent runs 'akf check' and builds on it. pip install akf
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
Software engineering discipline grounded in named primary standards (OWASP, ITIL, IETF RFCs, ISO, BABOK, SBAR, and more), shipped as a Claude Code plugin and an MCP server.
スター19実測 · 2026/10/20
モメンタム32.57 日 · 推定
MCP server for AI-to-AI collaboration — bridge Claude with Gemini, Codex, and other LLMs for code review, second opinions, and plan debate
スター18実測 · 2026/10/20
モメンタム327 日 · 推定
omo for Claude Code — native Claude Code plugin marketplace (Sisyphus Labs / omo): rules, comment-checker, LSP, ultrawork, ultragoal, start-work continuation, telemetry + bundled ast-grep & LSP MCP servers.
スター18実測 · 2026/10/20
モメンタム327 日 · 推定
A typed, replayable, budget-aware programming language for LLM agents. Compiles to Python & TypeScript.
スター18実測 · 2026/10/20
モメンタム327 日 · 推定
A MCP that turns PDFs into structured text, tables, figures, metadata, annotations, and page-level evidence for selective retrieval by MCP-compatible AI agents. Improves the information quality AI agents receive while also reducing token costs in some scenarios.
スター17実測 · 2026/10/20
モメンタム31.47 日 · 推定