Tag
The author shares a practical method for learning about AI evals by creating interactive skills for agents, encouraging a hands-on, experimental approach to learning.
A user shares a detailed prompt they used to train an AI to mimic their personal writing style, focusing on final-pass editing for communications like emails and social media.
This article introduces a meta-Skill designer called Dao-Skill, which helps users generate runnable and verifiable Agent Skills from chaotic requirements through six steps, more powerful than Codex's built-in Skill-creator.
Hermes Agent by Nous Research introduces /learn, a command that lets the agent deliberately create skills from documentation, code, or instructions without needing to first fail at the task, turning any source into a reusable skill.
This paper introduces W2S, a framework that automatically constructs executable Skills for LLM agents from historical interaction traces using a Skill-IR intermediate representation, improving behavioral replay consistency by 10.5% over baselines.
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.
The author released 'Skill Factory', a meta-skill for OpenClaw that provides a structured workflow for creating, iterating, and publishing skills, aiming to improve transparency and ease of construction.