Skill Libraries and Lifelong Learning (Voyager)
Voyager (Wang et al., TMLR 2024) treats executable code as a skill. Skills are named, retrievable, composable, and refined by environment feedback. This is the reference architecture for Claude Agent SDK skills, skillkit, and the 2026 skill-library pattern. Name Voyager's three components — automatic curriculum, skill library, iterative prompting — and the role of each. Explain why Voyager makes the action space code, not primitive commands. Implement a stdlib skill library with registration, retrieval, composition, and failure-driven refinement. Map Voyager's pattern onto the 2026 Claude Agent SDK skills and the skillkit ecosystem. Agents that rebuild every capability from scratch in every session do three things wrong: Waste tokens. Every task re-elicits the same reasoning. Lose progress. A correction learned in session A doesn't transfer to session B. Fail on long-horizon composition. Complex tasks need capability hierarchies; one-shot prompts cannot express them. Voyager's answer: treat each reusable capability as a named chunk of code stored in a library, retrievable by similarity, composable with other skills, and refined by execution feedback. Voyager (arXiv:2305.16291) structures an agent around: Automatic curriculum. A curiosity-driven proposer picks the next task based on the agent's current skill set and environment state. Exploration is bottom-up. Skill library. Each skill is executable code. New skills are added when a task succeeds. Skills are retrieved by query-to-description similarity. Iterative prompting…
Skill Libraries and Lifelong Learning (Voyager): Voyager (Wang et al., TMLR 2024) treats executable code as a skill. Skills are named, retrievable,…
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