WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
Liyan Tang and colleagues at Google separate raw execution experience, accumulated knowledge, and executable skills, keeping a persistent wiki that consolidates experience so later skill updates can build on it.
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Three layers with different write rules: Immutable execution traces are written once and never edited. A wiki layer of structured patterns and evolution logs compounds and never resets. A skill layer sits on top and gets reversible, conditional updates.
The loop closes on failures: An inference agent executes rollouts, a wiki maintainer runs root-cause analysis on the traces, a skill proposer drafts updates from the wiki and the traces, and a gating step evaluates the candidate on a validation set before anything ships. Bad updates roll back.
Evolved skills transfer: The method is model-agnostic, and skills evolved on one model carry over to smaller models that sometimes outperform bigger ones running without skills.
Why it matters: Most teams maintain skills by hand and discover the gaps in production. WikiSkill turns agent runs into the maintenance signal, and the practical takeaway generalizes past the paper. Build a persistent knowledge base across your projects, then use it to keep skills tuned.
Abstract
Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through interaction. However, the insights that guide skill development typically remain scattered across optimization histories, limiting their systematic reuse across iterations. We introduce WikiSkill, a framework that co-evolves agent skills with a persistent knowledge base (wiki). At a high level, WikiSkill separates raw execution experience, accumulated knowledge, and executable skills, while continuously consolidating experience into the wiki, which subsequent skill updates can build on. Across diverse benchmarks and models, WikiSkill consistently outperforms state-of-the-art skill-evolution methods and improves over no-skill baselines in most model-benchmark settings. We find that skill evolution complements model scaling: larger models generally benefit more from evolved skills, while smaller models with skills can outperform substantially larger models without them. We also find that evolved skills transfer effectively across models and model families, and skills evolved by other models can outperform self-evolved skills. Finally, our ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution. These results demonstrate the benefits of systematically accumulating and refining agent experience for developing reusable and transferable skills.