@Xudong07452910: Previously introduced SkillOpt, which is more about: how to repeatedly polish a skill into a more reliable "job description". The focus of this MUSE-Autoskill paper is different; it concerns how an Agent manages an entire skill library. The paper describes sk...
Summary
This MUSE-Autoskill paper focuses on how an Agent manages an entire skill library, placing skills into a complete lifecycle: creation, memory, management, evaluation, and re-optimization.
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Cached at: 05/29/26, 08:00 AM
We previously introduced SkillOpt, which focused more on how to repeatedly refine a single skill into a more reliable “job description.”
This paper on MUSE-Autoskill has a different focus—it concerns how an agent manages an entire skill library.
The paper places skills within a complete lifecycle: creation, memory, management, evaluation, and re-optimization.
When a task arrives, if no skill exists, create one; if a skill does exist, https://t.co/LX8gAJWQMZ
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@Xudong07452910: This SkillOpt paper is quite interesting—it actually addresses a very important point: AI agents in the future won't just rely on humans writing prompts; they can train their own 'job descriptions'. Currently, many skills/prompts are written one-off, and when real tasks pile up, various edge cases start to fail...
SkillOpt introduces a systematic controllable text-space optimizer that enables AI agents to train and improve their own skills (like 'work instructions') through iterative edits and validation, outperforming human-crafted and one-shot prompts across multiple benchmarks and models.
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
MUSE-Autoskill proposes a skill-centric agent framework that enables LLM agents to continuously create, reuse, and refine skills through a unified lifecycle of creation, memory, management, evaluation, and refinement. Experiments on SkillsBench show that lifecycle-managed skills improve task success, efficiency, reuse, and cross-agent transfer.
@bggg_ai: https://x.com/bggg_ai/status/2074018672113516624
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@NFTCPS: Microsoft came up with something called SkillOpt, and its approach is pretty wild: treating an agent's skill documentation like a neural network for training, with epochs, batches, learning rates, and validation sets, but without touching a single model weight. What makes it great? Let me break it down into three points: Training only modifies one skill document, and any new changes must be validated on the...
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@yaojingang: Built a Skill for interpreting Skills, open-sourced on GitHub. Also uploaded the original design of the interpreting Skill. This is a Skill for quality analysis, learning guidance, and usage recommendations within the Agent Skills ecosystem. Once executed, it converts a target Skill into structured analysis...
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