Turn Claude Code into its own Meta-Harness — a skill that evolves the scaffolding around a fixed model (memory, retrieval, context, prompts) via a native propose→score→Pareto loop. Native reimplementation of Meta-Harness (Lee et al. 2026).
Build portable agent teams across models and harnesses, with shared memory, skills, tools, and workflows — then bring them into Codex, Claude Code, or any AI system you build. 构建跨 harness 和模型的可移植 Agent Team，共享记忆、技能、工具和工作流，一次构建，接入 Codex、Claude Code 或任何 AI 系统。
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
Builds up to a goal in a field you have no expertise in: splits it into the foundations that goal actually needs, mobilizes what you already hold, learns what is missing, and leaves every solved path behind for the next goal. Nothing is called done until a check passes.
