Skill++ 论文提出改写技能描述，解决智能体路由冲突
给智能体加新技能总把旧技能挤坏的可以看看这篇，Skill++ 只改名字和描述就把命中率从 36.4% 提到 76.3%，不用训练。
论文针对一个常见问题：给智能体添加新技能后，路由器可能因名称和描述相似而挤掉旧技能，即使旧技能本身没改动。Skill++ 的做法是只重写新技能的名称和描述，说明它支持什么、与最接近的已有技能有何区别、排除了什么，不需要训练路由器也不用标注数据。在技能库增长到 731 个、跨越十个会话的测试中，路由器对 76.3% 的查询选中了全部所需技能，而用原始元数据时只有 36.4%。结果在三个基准和五个路由器上成立，并可迁移到 Codex CLI 环境。
Great paper on scaling agent skills.
It includes some great tips for keeping your agent skill library updated and running smoothly.
(bookmark it)
You will often see that adding a new skill to an agent can break routing for older skills, even when nothing about the older skills or the router changed.
Routers pick skills by name and description. When a new skill has similar metadata, it can replace or crowd out an earlier one.
Skill++ rewrites only the name and description of each incoming skill. The new description says what the skill supports, how it differs from its closest existing skill, and what it excludes. There is no router training and no labeled data.
As a library grows to 731 skills over ten sessions, the router selects every required skill for 76.3% of queries, against 36.4% with the original metadata. Substitution errors nearly disappear, and crowding errors fall by half.
The gains hold across three benchmarks and five routers, and they carry over to the Codex CLI harness.
When you add a skill, describe how it differs from the closest skill you already have.
Paper: https://t.co/QH2cqN0BE0
Chat with Paper: https://t.co/Yhxakck1no
