Generate type-safe AxLearn code with @ax-llm/ax for adaptive agents.
npx skills add https://github.com/jadecli/researchers --skill ax-learn
Please help me install this Agent Skill. Skill: ax-learn Source: https://github.com/jadecli/researchers/tree/main/agentcrawls-ts/.claude/skills/ax-learn Command: npx skills add https://github.com/jadecli/researchers --skill ax-learn
The AxLearn coding assistant helps generate correct AxLearn code using the @ax-llm/ax library, enabling reliable implementation of self-improving agents and learning loops.
Create and configure an AxLearn agent with storage and teacher, initialize runtime components, and run a simple continuous update cycle.
High-intent search queries and answers about installing and using this skill.
Generate self-improving agent code by configuring type-safe AxLearn generators with required name, storage, teacher, and runtimeAI parameters. The assistant produces correct @ax-llm/ax library patterns for building adaptive learning loops with auditable checkpoints.
AxLearn runtime modes include batch, continuous, and playbook configurations for feedback-aware updates. Each mode supports trace-backed learning patterns, allowing adaptive agents to process feedback and execute self-improving update cycles across different runtime environments.
Set up trace-backed learning by initializing AxLearn storage and teacher components, then configuring runtimeAI for feedback-aware updates. The runtime processes agent traces and applies feedback loops to enable continuous self-improvement across supported batch, continuous, and playbook modes.
Yes, AxLearn codegen requires providing name, storage, teacher, and runtimeAI configurations for relevant operations. These components establish the foundation for trace-backed learning loops, feedback-aware updates, and auditable checkpoints needed for adaptive agent workflows.
Build adaptive agents by creating and configuring AxLearn components with storage and teacher, initializing runtime elements, then running a continuous update cycle. This approach supports feedback-aware updates and self-improving loops with auditable checkpoints across multiple runtime modes.
Yes, AxLearn codegen supports both batch and continuous learning workflows alongside playbook modes. Each mode generates type-safe configurations, generators, and runtime patterns for adaptive agents, enabling trace-backed learning and feedback-aware updates across different processing requirements.
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