feat: Integrate Learning Atomizer Core (LAC) and master instructions

Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.

## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
  and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions

## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
  error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern

## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

knowledge_base/__init__.py

@@ -0,0 +1,17 @@
+"""
+Knowledge Base Package
+======================
+
+Provides persistent knowledge storage for Atomizer including:
+- LAC (Learning Atomizer Core): Session insights, optimization memory, skill evolution
+- Research sessions: Detailed research logs
+"""
+
+from .lac import LearningAtomizerCore, get_lac, record_insight, query_insights
+
+__all__ = [
+    "LearningAtomizerCore",
+    "get_lac",
+    "record_insight",
+    "query_insights",
+]