#agent-skills

4 articles

01 ·Paper Deep-Dives·★ MEMBER·PAPER·7 min read Paper Explained: EVISKILL — Rewriting an Agent's Playbook from Replayable Evidence A framework for automatically revising an LLM agent's skill document that pins every proposed edit to the exact trajectory steps that motivated it, then re-runs only those steps to check the edit actually works. It posts the best accuracy in 14 of 18 benchmark–model settings. 02 ·RAG & Retrieval·★ MEMBER·PAPER·8 min read Paper Explained: Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation — Borrow Someone Else's Skill, Rewrite It for Your Own Harness RASO retrieves agent skills from a large public corpus and rewrites them in the vocabulary of the target execution harness before using them. With a rollout-free initialization stage and a failure-driven update stage, it beat retrieval-free optimizers across four benchmarks and two models. A from-scratch walkthrough of the paper. 03 ·Agents·★ MEMBER·PAPER·15 min read Paper Walkthrough: Training Agents to Evolve with Their Harness Why compact models break when you rewrite prompts and tool definitions every week, and what the team behind Taobao Live's AI avatar streamers does about it. A ground-up walkthrough of Harness-Aware Training, from intuition to equations to measured numbers and stated limits. 04 ·Agents·★ MEMBER·PAPER·12 min read Paper Explained: Repo-To-Skill — Distilling GitHub Repositories Into Skills an AI Can Use A third layer that is neither the model nor the harness: operational knowledge, distilled from 1,000 GitHub repositories into 5,353 verified skills. What DisCo and the AREX-Skill Library actually do, what 31.11%→72.89% on MLE-bench is made of, and where the skills failed.