docs: Final documentation polish and consistency fixes

- Update README.md with LLM assistant section
- Create optimization_memory JSONL structure
- Move implementation plans from skills/modules to docs/plans
- Verify all imports work correctly

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

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

README.md

@@ -315,6 +315,25 @@ Atomizer/
 
 ---
 
+## For AI Assistants
+
+Atomizer is designed for LLM-first interaction. Key resources:
+
+- **[CLAUDE.md](CLAUDE.md)** - System instructions for Claude Code
+- **[.claude/skills/](/.claude/skills/)** - LLM skill modules
+- **[docs/protocols/](docs/protocols/)** - Protocol Operating System
+
+### Knowledge Base (LAC)
+
+The Learning Atomizer Core (`knowledge_base/lac/`) accumulates optimization knowledge:
+- `session_insights/` - Learnings from past sessions
+- `optimization_memory/` - Optimization outcomes by geometry type
+- `playbook.json` - ACE framework knowledge store
+
+For detailed AI interaction guidance, see CLAUDE.md.
+
+---
+
 ## Environment
 
 **Critical**: Always use the `atomizer` conda environment:

knowledge_base/lac/optimization_memory/beam.jsonl

@@ -0,0 +1 @@
+{"geometry_type": "beam", "study_name": "example", "method": "TPE", "objectives": ["mass"], "trials": 0, "converged": false, "notes": "Schema file - replace with real data"}

knowledge_base/lac/optimization_memory/bracket.jsonl

@@ -0,0 +1 @@
+{"geometry_type": "bracket", "study_name": "example", "method": "TPE", "objectives": ["mass"], "trials": 0, "converged": false, "notes": "Schema file - replace with real data"}

knowledge_base/lac/optimization_memory/mirror.jsonl

@@ -0,0 +1 @@
+{"geometry_type": "mirror", "study_name": "m1_mirror_cost_reduction_flat_back_V9", "method": "SAT_v3", "objectives": ["wfe_40_20", "mass_kg"], "trials": 100, "converged": true, "best_weighted_sum": 205.58, "notes": "SAT v3 achieved WS=205.58 (new campaign record)"}