084dcdef31
Some checks failed
- 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
161 lines
4.0 KiB
Markdown
161 lines
4.0 KiB
Markdown
---
|
|
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
|