cross-task-learner

# Cross-Task Learner Skill Enable agent loops to learn from similar past tasks and share discovered patterns across multiple concurrent or sequential loops. **Research Foundation**: REF-013 MetaGPT - 159% improvement with shared state **Version 2.0**: Multi-loop awareness with loop_id tracking --- ## Overview This skill provides two core capabilities: 1. **Pattern Extraction** - On loop completion, extract reusable patterns from execution history 2. **Pattern Injection** - On loop start, inject relevant patterns from previous loops ### Benefits | Benefit | Impact | |---------|--------| | Faster resolution | Patterns eliminate redundant debugging | | Higher success rates | Proven approaches applied automatically | | Accumulated wisdom | System gets smarter over time | | Anti-pattern detection | Failed approaches flagged and avoided | ### Research Basis From REF-013 MetaGPT: - **159% improvement** with shared state across agents - **Publish-subscribe pattern** enables knowledge sharing - **Structured outputs** become inputs for other agents - **Memory persistence** critical for cross-session learning --- ## Pattern Extraction (On Loop Completion) ### Trigger - Agent loop completion (success, partial, or failure) - Manual extraction request via `aiwg ralph-extract-patterns {loop_id}` ### Process ```yaml extraction_steps: 1_analyze_loop_history: - Load loop state from .aiwg/ralph/loops/{loop_id}/state.json - Load iteration analytics - Load debug mem...