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triple-loop-learninglisted

(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 persisting.
richfrem/agent-plugins-skills · ★ 3 · AI & Automation · score 67
Install: claude install-skill richfrem/agent-plugins-skills
## 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 Orchestrator** governs a long-horizon pipeline of execution, planning, and tactical problem-solving. This architecture is entirely framework-agnostic. While originally developed for `agent-agentic-os`, it models the core loop defined by Meta-Harness research where autonomous systems evolve their own operating instructions based strictly on headless evaluators. ## Architecture Overview ```mermaid flowchart TD subgraph Outer["Outer Loop (Meta-Learning & Orchestration)"] Hypothesize[Hypothesis Generation] --> StrategyBridge[Strategy Packet] Report --> EvalBridge[Score Analysis] EvalBridge --> Conclude[Accept / Reject Hypothesis] end subgraph Mid["Strategic Planner (Dual-Loop Integration)"] Plan[Define Sub-tasks] --> TacticalBridge[Handoff Packet] Result[Aggregate Results] --> Report[Generate Report] end subgraph Inner["Tactical Executor (Single-Loop Integration)"] Execute[Code Mutation] --> Test[Headless Evaluation]