feat: Add TrialManager and DashboardDB for unified trial management

- Add TrialManager (trial_manager.py) for consistent trial_NNNN naming
- Add DashboardDB (dashboard_db.py) for Optuna-compatible database schema
- Update CLAUDE.md with trial management documentation
- Update ATOMIZER_CONTEXT.md with v1.8 trial system
- Update cheatsheet v2.2 with new utilities
- Update SYS_14 protocol to v2.3 with TrialManager integration
- Add LAC learnings for trial management patterns
- Add archive/README.md for deprecated code policy

Key principles:
- Trial numbers NEVER reset (monotonic)
- Folders NEVER get overwritten
- Database always synced with filesystem
- Surrogate predictions are NOT trials (only FEA results)

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

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

.claude/skills/01_CHEATSHEET.md

@@ -1,19 +1,21 @@
 ---
 skill_id: SKILL_001
-version: 2.1
-last_updated: 2025-12-22
+version: 2.2
+last_updated: 2025-12-28
 type: reference
 code_dependencies:
   - optimization_engine/extractors/__init__.py
   - optimization_engine/method_selector.py
+  - optimization_engine/utils/trial_manager.py
+  - optimization_engine/utils/dashboard_db.py
 requires_skills:
   - SKILL_000
 ---
 
 # Atomizer Quick Reference Cheatsheet
 
-**Version**: 2.1
-**Updated**: 2025-12-22
+**Version**: 2.2
+**Updated**: 2025-12-28
 **Purpose**: Rapid lookup for common operations. "I want X → Use Y"
 
 ---
@@ -406,3 +408,75 @@ class FEARunner:
 **Reference implementations**:
 - `studies/m1_mirror_adaptive_V14/run_optimization.py`
 - `studies/m1_mirror_adaptive_V15/run_optimization.py`
+
+---
+
+## Trial Management Utilities
+
+### TrialManager - Unified Trial Folder + DB Management
+
+```python
+from optimization_engine.utils.trial_manager import TrialManager
+
+tm = TrialManager(study_dir)
+
+# Start new trial (creates folder, saves params)
+trial = tm.new_trial(
+    params={'rib_thickness': 10.5, 'mirror_face_thickness': 17.0},
+    source="turbo",
+    metadata={'turbo_batch': 1, 'predicted_ws': 42.0}
+)
+# Returns: {'trial_id': 47, 'trial_number': 47, 'folder_path': Path(...)}
+
+# After FEA completes
+tm.complete_trial(
+    trial_number=trial['trial_number'],
+    objectives={'wfe_40_20': 5.63, 'mass_kg': 118.67},
+    weighted_sum=42.5,
+    is_feasible=True
+)
+
+# Mark failed trial
+tm.fail_trial(trial_number=47, error="NX solver timeout")
+```
+
+### DashboardDB - Optuna-Compatible Database
+
+```python
+from optimization_engine.utils.dashboard_db import DashboardDB, convert_custom_to_optuna
+
+# Create new dashboard-compatible database
+db = DashboardDB(db_path, study_name="my_study")
+
+# Log a trial
+trial_id = db.log_trial(
+    params={'rib_thickness': 10.5},
+    objectives={'wfe_40_20': 5.63, 'mass_kg': 118.67},
+    weighted_sum=42.5,
+    is_feasible=True,
+    state="COMPLETE"
+)
+
+# Mark best trial
+db.mark_best(trial_id)
+
+# Get summary
+summary = db.get_summary()
+
+# Convert existing custom database to Optuna format
+convert_custom_to_optuna(db_path, study_name)
+```
+
+### Trial Naming Convention
+
+```
+2_iterations/
+├── trial_0001/    # Zero-padded, monotonically increasing
+├── trial_0002/    # NEVER reset, NEVER overwritten
+└── trial_0003/
+```
+
+**Key principles**:
+- Trial numbers **NEVER reset** across study lifetime
+- Surrogate predictions (5K per batch) are NOT logged as trials
+- Only FEA-validated results become trials