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triple-loop-learning

(Industry standard: Meta-Learning System / Automated Autoresearch) Primary Use Case: Continuous, self-improving orchestration of an agentic system over multiple sessions. Use when: building a continuous improvement layer that autonomously identifies workflow friction, postulates hypotheses, and tests improved instructions/coding skills against an objective headless benchmark before merging and per

3estrelas
Atualizado há 4 meses

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Ler descrição completa

Dependencies

This skill requires Python 3.8+ and standard library only.

Evaluation gate: NOT included in this primitive. The calling system (e.g., agent-agentic-os os-improvement-loop) is responsible for wrapping this skill with an eval gate and experiment log.

Triple-Loop Learning (Meta-Learning System)

This skill defines the orchestration pattern for the Triple-Loop Architecture. Pattern 5 is a robust, autonomous feedback loop where an independent **Meta-Learning Orche

[Description truncada. Veja o README completo no GitHub.]

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