feat: Implement ACE Context Engineering framework (SYS_17)

Complete implementation of Agentic Context Engineering (ACE) framework:

Core modules (optimization_engine/context/):
- playbook.py: AtomizerPlaybook with helpful/harmful scoring
- reflector.py: AtomizerReflector for insight extraction
- session_state.py: Context isolation (exposed/isolated state)
- feedback_loop.py: Automated learning from trial results
- compaction.py: Long-session context management
- cache_monitor.py: KV-cache optimization tracking
- runner_integration.py: OptimizationRunner integration

Dashboard integration:
- context.py: 12 REST API endpoints for playbook management

Tests:
- test_context_engineering.py: 44 unit tests
- test_context_integration.py: 16 integration tests

Documentation:
- CONTEXT_ENGINEERING_REPORT.md: Comprehensive implementation report
- CONTEXT_ENGINEERING_API.md: Complete API reference
- SYS_17_CONTEXT_ENGINEERING.md: System protocol
- Updated cheatsheet with SYS_17 quick reference
- Enhanced bootstrap (00_BOOTSTRAP_V2.md)

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

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

.claude/skills/01_CHEATSHEET.md

@@ -34,6 +34,7 @@ requires_skills:
 | Add custom physics extractor | EXT_01 | Create in `optimization_engine/extractors/` |
 | Add lifecycle hook | EXT_02 | Create in `optimization_engine/plugins/` |
 | Generate physics insight | SYS_16 | `python -m optimization_engine.insights generate <study>` |
+| **Manage knowledge/playbook** | **SYS_17** | `from optimization_engine.context import AtomizerPlaybook` |
 
 ---
 
@@ -366,6 +367,7 @@ Without it, `UpdateFemodel()` runs but the mesh doesn't change!
 | 14 | Neural | Surrogate model acceleration |
 | 15 | Method Selector | Recommends optimization strategy |
 | 16 | Study Insights | Physics visualizations (Zernike, stress, modal) |
+| 17 | Context Engineering | ACE framework - self-improving knowledge system |
 
 ---
 
@@ -549,3 +551,106 @@ convert_custom_to_optuna(db_path, study_name)
 - Trial numbers **NEVER reset** across study lifetime
 - Surrogate predictions (5K per batch) are NOT logged as trials
 - Only FEA-validated results become trials
+
+---
+
+## Context Engineering Quick Reference (SYS_17)
+
+The ACE (Agentic Context Engineering) framework enables self-improving optimization through structured knowledge capture.
+
+### Core Components
+
+| Component | Purpose | Key Function |
+|-----------|---------|--------------|
+| **AtomizerPlaybook** | Structured knowledge store | `playbook.add_insight()`, `playbook.get_context_for_task()` |
+| **AtomizerReflector** | Extracts insights from outcomes | `reflector.analyze_outcome()` |
+| **AtomizerSessionState** | Context isolation (exposed/isolated) | `session.get_llm_context()` |
+| **FeedbackLoop** | Automated learning | `feedback.process_trial_result()` |
+| **CompactionManager** | Long-session handling | `compactor.maybe_compact()` |
+| **CacheMonitor** | KV-cache optimization | `optimizer.track_completion()` |
+
+### Python API Quick Reference
+
+```python
+from optimization_engine.context import (
+    AtomizerPlaybook, AtomizerReflector, get_session,
+    InsightCategory, TaskType, FeedbackLoop
+)
+
+# Load playbook
+playbook = AtomizerPlaybook.load(Path("knowledge_base/playbook.json"))
+
+# Add an insight
+playbook.add_insight(
+    category=InsightCategory.STRATEGY,  # str, mis, tool, cal, dom, wf
+    content="CMA-ES converges faster on smooth mirror surfaces",
+    tags=["mirror", "sampler", "convergence"]
+)
+playbook.save(Path("knowledge_base/playbook.json"))
+
+# Get context for LLM
+context = playbook.get_context_for_task(
+    task_type="optimization",
+    max_items=15,
+    min_confidence=0.5
+)
+
+# Record feedback
+playbook.record_outcome(item_id="str_001", helpful=True)
+
+# Session state
+session = get_session()
+session.exposed.task_type = TaskType.RUN_OPTIMIZATION
+session.add_action("Started optimization run")
+llm_context = session.get_llm_context()
+
+# Feedback loop (automated learning)
+feedback = FeedbackLoop(playbook_path)
+feedback.process_trial_result(
+    trial_number=42,
+    params={'thickness': 10.5},
+    objectives={'mass': 5.2},
+    is_feasible=True
+)
+```
+
+### Insight Categories
+
+| Category | Code | Use For |
+|----------|------|---------|
+| Strategy | `str` | Optimization approaches that work |
+| Mistake | `mis` | Common errors to avoid |
+| Tool | `tool` | Tool usage patterns |
+| Calculation | `cal` | Formulas and calculations |
+| Domain | `dom` | FEA/NX domain knowledge |
+| Workflow | `wf` | Process patterns |
+
+### Playbook Item Format
+
+```
+[str_001] helpful=5 harmful=0 :: CMA-ES converges faster on smooth surfaces
+```
+
+- `net_score = helpful - harmful`
+- `confidence = helpful / (helpful + harmful)`
+- Items with `net_score < -3` are pruned
+
+### REST API Endpoints
+
+| Endpoint | Method | Purpose |
+|----------|--------|---------|
+| `/api/context/playbook` | GET | Playbook summary stats |
+| `/api/context/playbook/items` | GET | List items with filters |
+| `/api/context/playbook/feedback` | POST | Record helpful/harmful |
+| `/api/context/playbook/insights` | POST | Add new insight |
+| `/api/context/playbook/prune` | POST | Remove harmful items |
+| `/api/context/session` | GET | Current session state |
+| `/api/context/learning/report` | GET | Comprehensive learning report |
+
+### Dashboard URL
+
+| Service | URL | Purpose |
+|---------|-----|---------|
+| Context API | `http://localhost:5000/api/context` | Playbook management |
+
+**Full documentation**: `docs/protocols/system/SYS_17_CONTEXT_ENGINEERING.md`