elvis@omarsar0

2026-07-02 05:22·1天前

AI 摘要

论文提出SkillComposer，将代码Agent的技能选择与组合视为一次联合决策，用约束自回归解码器一次生成完整技能计划（包括技能、数量与顺序），自然处理技能间依赖。在SkillsBench上，使用GPT-5.2-Codex和Gemini-3-Pro-Preview，pass rate分别提升+23.1和+18.2个百分点，超过top-3检索，并以更低prompt token成本匹配gold-skill上界。

Great paper on managing agent skills.

Skill libraries keep growing， and picking the right skills has become a bottleneck for coding agents.

The defaults are to expose the agent to the whole skill collection， or retrieve skills with embeddings and rerankers. Both treat the choice as independent picks.

SkillComposer treats composition as one joint decision over which skills， how many， and in what order. A constrained autoregressive decoder over skill identifiers produces the full plan in a single pass， so dependencies between successive skills fall out naturally.

On SkillsBench with GPT-5.2-Codex and Gemini-3-Pro-Preview， it lifts pass rate by +23.1 and +18.2pp over no-skill， beats top-3 retrieval， and matches the gold-skill upper bound at lower prompt-token cost.

Paper： https://arxiv.org/abs/2606.32025

Learn to build effective AI agents in our academy： https://academy.dair.ai/

elvis@omarsar0 · X

2026-07-02 05:22·1天前

AI 摘要

Great paper on managing agent skills.

Skill libraries keep growing， and picking the right skills has become a bottleneck for coding agents.

The defaults are to expose the agent to the whole skill collection， or retrieve skills with embeddings and rerankers. Both treat the choice as independent picks.