docs: update refactoring summary and mastery plan to reflect completion of all 11 atomic skills

- Mark Phase 2 as completed with all 11/11 atomic skills created
- Update skill counts: AITBC skills (6/6), OpenClaw skills (5/5)
- Move aitbc-node-coordinator and aitbc-analytics-analyzer from remaining to completed
- Update Phase 3 status from PLANNED to IN PROGRESS
- Add Gitea-based node synchronization documentation (replaces SCP)
- Clarify two-node architecture with same port (8006) on different I

.windsurf/skills/openclaw-performance-optimizer.md

@@ -0,0 +1,160 @@
+---
+description: Atomic OpenClaw agent performance tuning and optimization with deterministic outputs
+title: openclaw-performance-optimizer
+version: 1.0
+---
+
+# OpenClaw Performance Optimizer
+
+## Purpose
+Optimize agent performance, tune execution parameters, and improve efficiency for OpenClaw agents through systematic analysis and adjustment.
+
+## Activation
+Trigger when user requests performance optimization: agent tuning, parameter adjustment, efficiency improvements, or performance benchmarking.
+
+## Input
+```json
+{
+  "operation": "tune|benchmark|optimize|profile",
+  "agent": "agent_name",
+  "target": "speed|memory|throughput|latency|all",
+  "parameters": {
+    "max_tokens": "number (optional)",
+    "temperature": "number (optional)",
+    "timeout": "number (optional)"
+  }
+}
+```
+
+## Output
+```json
+{
+  "summary": "Agent performance optimization completed successfully",
+  "operation": "tune|benchmark|optimize|profile",
+  "agent": "agent_name",
+  "target": "speed|memory|throughput|latency|all",
+  "before_metrics": {
+    "execution_time": "number",
+    "memory_usage": "number",
+    "throughput": "number",
+    "latency": "number"
+  },
+  "after_metrics": {
+    "execution_time": "number",
+    "memory_usage": "number",
+    "throughput": "number",
+    "latency": "number"
+  },
+  "improvement": {
+    "speed": "percentage",
+    "memory": "percentage",
+    "throughput": "percentage",
+    "latency": "percentage"
+  },
+  "issues": [],
+  "recommendations": [],
+  "confidence": 1.0,
+  "execution_time": "number",
+  "validation_status": "success|partial|failed"
+}
+```
+
+## Process
+
+### 1. Analyze
+- Profile current agent performance
+- Identify bottlenecks
+- Assess optimization opportunities
+- Validate agent state
+
+### 2. Plan
+- Select optimization strategy
+- Define parameter adjustments
+- Set performance targets
+- Plan validation approach
+
+### 3. Execute
+- Apply parameter adjustments
+- Run performance benchmarks
+- Measure improvements
+- Validate stability
+
+### 4. Validate
+- Verify performance gains
+- Check for regressions
+- Validate parameter stability
+- Confirm agent functionality
+
+## Constraints
+- **MUST NOT** modify agent core functionality
+- **MUST NOT** exceed 90 seconds for optimization
+- **MUST** validate parameter ranges
+- **MUST** preserve agent behavior
+- **MUST** rollback on critical failures
+
+## Environment Assumptions
+- Agent operational and accessible
+- Performance monitoring available
+- Parameter configuration accessible
+- Benchmarking tools available
+- Agent state persistence functional
+
+## Error Handling
+- Parameter validation failure → Revert to previous parameters
+- Performance regression → Rollback optimization
+- Agent instability → Restore baseline configuration
+- Timeout during optimization → Return partial results
+
+## Example Usage Prompt
+
+```
+Optimize main agent for speed and memory efficiency
+```
+
+## Expected Output Example
+
+```json
+{
+  "summary": "Main agent optimized for speed and memory efficiency",
+  "operation": "optimize",
+  "agent": "main",
+  "target": "all",
+  "before_metrics": {
+    "execution_time": 15.2,
+    "memory_usage": 250,
+    "throughput": 8.5,
+    "latency": 2.1
+  },
+  "after_metrics": {
+    "execution_time": 11.8,
+    "memory_usage": 180,
+    "throughput": 12.3,
+    "latency": 1.5
+  },
+  "improvement": {
+    "speed": "22%",
+    "memory": "28%",
+    "throughput": "45%",
+    "latency": "29%"
+  },
+  "issues": [],
+  "recommendations": ["Consider further optimization for memory-intensive tasks"],
+  "confidence": 1.0,
+  "execution_time": 35.7,
+  "validation_status": "success"
+}
+```
+
+## Model Routing Suggestion
+
+**Reasoning Model** (Claude Sonnet, GPT-4)
+- Complex parameter optimization
+- Performance analysis and tuning
+- Benchmark interpretation
+- Regression detection
+
+**Performance Notes**
+- **Execution Time**: 20-60 seconds for optimization, 5-15 seconds for benchmarking
+- **Memory Usage**: <200MB for optimization operations
+- **Network Requirements**: Agent communication for profiling
+- **Concurrency**: Safe for sequential optimization of different agents