不用换大脑，给AI装个万能插线板
现在的AI助手看着全能，其实每种能力都是单独装的：能聊天的不会P图，会P图的不懂代码，想让它做一条“先查资料、再画图、最后配音”的完整流程，得你手动把几个工具拼起来，中间任何一步出错都得重来。这篇提出一个“外挂”思路：不动AI的推理核心，只给它加一套标准化的“插线板”——把文字、图片、音频、视频、3D模型、代码这些不同格式统一成一套接口，再配一个“资产仓库”记住中间产物，让AI能像流水线一样自动调度：先并行处理互不依赖的步骤，再把结果传给下一步，跨轮对话也能复用。实测中，原本只能处理四成输入格式的两个主流模型，装上这个外挂后直接变成百分之百，综合质量分数翻了两倍多。它不是你明天就能用上的产品，但它指出了一个趋势：未来AI的进化可能不再靠换更大的模型，而是靠给现有模型不断加装这种可替换的“技能模块”。
📄 原文摘要(英文)
General-purpose agents can plan, reason, and act over long horizons, yet their production capabilities remain fragmented across text, images, audio, video, documents, 3D assets, and code. Extending a foundation model to additional modalities ties capability growth to costly model updates, while assembling specialist models and tools leaves unresolved how procedures, dependencies, intermediate assets, and cross-turn revisions should be coordinated. We present Omni-IO Skills, a plug-and-play Agent Harness that makes existing agents omni-native through hierarchical Skills, a standardized multimodal execution interface, dependency-aware orchestration, and a persistent Asset Registry. Multi-asset workflows are represented as Declare Execution Graphs, which schedule independent operations concurrently and register successful outputs for downstream and cross-turn reuse across replaceable execution backends. Its 27 Skills cover 38 representative tasks spanning seven artifact modalities and four capability families: understanding, generation, reasoning, and retrieval. On UniM-90, the harness raises the input-support rates of GPT-5.6 Sol and Claude Sonnet 5 from 40.00% and 38.89% to 100%, while increasing relative Semantic--Quality Coupled Score from 26.99 to 74.94 and from 27.82 to 77.78, respectively; Strict Structure Score reaches 100.00 and 99.78. These results establish harness-level capability composition as a practical route to broad, evolvable Omni systems without changing the host agent's reasoning core.