metacognitive-self-mod

# Metacognitive Self-Modification Analyze the effectiveness of past skill improvements and refine the improvement process itself. This is the core innovation from the Hyperagents paper: not just improving skills, but improving HOW skills are improved. ## Context Triggers (auto-invocation) This skill should be invoked automatically when: 1. **Regression detected**: The homeostatic monitor finds a skill's evaluation window ended in `pending_rollback_review` status. The improvement made things worse, and we need to understand why. 2. **Low effectiveness rate**: When `ImprovementMemory.get_effective_strategies()` vs `get_failed_strategies()` shows effectiveness below 50%, the improvement process itself needs refinement. 3. **Degradation despite improvements**: When `PerformanceTracker.get_improvement_trend()` returns negative for a skill that was recently improved. 4. **Periodic check**: After every 10 improvement cycles (tracked via outcome count in ImprovementMemory). ### Hook integration The homeostatic monitor emits `"improvement_triggered": true` when a skill crosses the flag threshold. At that point, before dispatching the skill-improver, check if metacognitive analysis is warranted: ```python from abstract.improvement_memory import ImprovementMemory from pathlib import Path memory = ImprovementMemory( Path.home() / ".claude/skills/improvement_memory.json" ) # Check if metacognitive analysis is warranted effective = memory.get_effe...