Routing Optimization Skill
版本: v1.0 创建日期: 2026-03-26 作者: 象腿 (main agent) 用途: 基于实际使用数据优化routing规则，提升调度准确性
🎯 核心功能
Routing Optimization skill负责：
- 数据收集: 收集routing命中率和准确率数据
- 规则分析: 分析哪些规则频繁命中/未命中
- A/B测试: 对比不同routing策略的效果
- 规则优化: 基于数据优化routing规则
- 动态调整: 支持动态优先级调整
📋 优化策略
策略1: 命中率分析
目标: 识别高命中率和低命中率的规则
方法:
def analyze_hit_rate(dispatch_logs):
"""
分析routing规则的命中率
Args:
dispatch_logs: 调度日志列表
Returns:
dict: 各规则的命中率统计
"""
rule_stats = {}
for log in dispatch_logs:
rule_name = log.get('matched_rule')
if not rule_name:
continue
if rule_name not in rule_stats:
rule_stats[rule_name] = {
'hits': 0,
'success': 0,
'patterns': set()
}
rule_stats[rule_name]['hits'] += 1
if log.get('success'):
rule_stats[rule_name]['success'] += 1
rule_stats[rule_name]['patterns'].add(log.get('matched_pattern'))
# 计算成功率
for rule_name in rule_stats:
stats = rule_stats[rule_name]
stats['success_rate'] = stats['success'] / stats['hits'] if stats['hits'] > 0 else 0
return rule_stats输出示例:
{
"coder": {
"hits": 50,
"success": 48,
"success_rate": 0.96,
"patterns": ["debug", "code", "github"]
},
"danao": {
"hits": 30,
"success": 28,
"success_rate": 0.93,
"patterns": ["search", "research", "study"]
},
"self": {
"hits": 20,
"success": 18,
"success_rate": 0.90,
"patterns": [".*"]
}
}策略2: 准确率分析
目标: 识别routing规则的准确性
方法:
def analyze_routing_accuracy(dispatch_logs):
"""
分析routing规则的准确性
Args:
dispatch_logs: 调度日志列表
Returns:
dict: 各规则的准确率统计
"""
rule_accuracy = {}
for log in dispatch_logs:
rule_name = log.get('matched_rule')
if not rule_name:
continue
if rule_name not in rule_accuracy:
rule_accuracy[rule_name] = {
'correct': 0,
'incorrect': 0,
'fallback': 0
}
# 判断是否正确路由
if log.get('user_satisfied'):
rule_accuracy[rule_name]['correct'] += 1
elif log.get('fallback'):
rule_accuracy[rule_name]['fallback'] += 1
else:
rule_accuracy[rule_name]['incorrect'] += 1
# 计算准确率
for rule_name in rule_accuracy:
stats = rule_accuracy[rule_name]
total = stats['correct'] + stats['incorrect'] + stats['fallback']
stats['accuracy'] = stats['correct'] / total if total > 0 else 0
return rule_accuracy策略3: A/B测试
目标: 对比不同routing策略的效果
方法:
def ab_test_routing(routing_a, routing_b, test_queries):
"""
A/B测试两个routing策略
Args:
routing_a: 策略A的routing规则
routing_b: 策略B的routing规则
test_queries: 测试查询列表
Returns:
dict: A/B测试结果
"""
results_a = []
results_b = []
for query in test_queries:
# 使用策略A路由
route_a = match_route(query, routing_a)
results_a.append({
'query': query,
'route': route_a,
'user_rating': None # 待用户评分
})
# 使用策略B路由
route_b = match_route(query, routing_b)
results_b.append({
'query': query,
'route': route_b,
'user_rating': None
})
# 对比结果
comparison = {
'strategy_a': {
'avg_accuracy': calculate_avg_accuracy(results_a),
'avg_dispatch_time': calculate_avg_time(results_a)
},
'strategy_b': {
'avg_accuracy': calculate_avg_accuracy(results_b),
'avg_dispatch_time': calculate_avg_time(results_b)
},
'winner': None # 待确定
}
# 确定优胜者
if comparison['strategy_b']['avg_accuracy'] > comparison['strategy_a']['avg_accuracy']:
comparison['winner'] = 'B'
elif comparison['strategy_b']['avg_accuracy'] < comparison['strategy_a']['avg_accuracy']:
comparison['winner'] = 'A'
else:
# 准确率相同，对比速度
if comparison['strategy_b']['avg_dispatch_time'] < comparison['strategy_a']['avg_dispatch_time']:
comparison['winner'] = 'B'
else:
comparison['winner'] = 'A'
return comparison策略4: 动态优先级
目标: 根据实时数据动态调整routing优先级
方法:
def adjust_routing_priority(rule_stats):
"""
根据统计数据调整routing优先级
Args:
rule_stats: 规则统计字典
Returns:
list: 调整后的routing规则列表
"""
adjusted_rules = []
for rule_name, stats in rule_stats.items():
# 基础优先级
base_priority = get_base_priority(rule_name)
# 根据成功率调整
if stats['success_rate'] > 0.95:
# 高成功率，降低优先级（更快匹配）
adjusted_priority = base_priority - 1
elif stats['success_rate'] < 0.85:
# 低成功率，提高优先级（给更多机会）
adjusted_priority = base_priority + 1
else:
# 正常成功率，保持不变
adjusted_priority = base_priority
adjusted_rules.append({
'rule_name': rule_name,
'original_priority': base_priority,
'adjusted_priority': adjusted_priority,
'reason': f"Success rate: {stats['success_rate']:.2f}"
})
# 按调整后的优先级排序
adjusted_rules.sort(key=lambda x: x['adjusted_priority'])
return adjusted_rules🛠️ PowerShell实现
PowerShell命中率分析
function Analyze-RoutingHitRate {
param(
[array]$DispatchLogs
)
$ruleStats = @{}
foreach ($log in $DispatchLogs) {
$ruleName = $log.matched_rule
if (-not $ruleName) {
continue
}
if (-not $ruleStats.ContainsKey($ruleName)) {
$ruleStats[$ruleName] = @{
Hits = 0
Success = 0
Patterns = @{}
}
}
$ruleStats[$ruleName].Hits++
if ($log.success) {
$ruleStats[$ruleName].Success++
}
$pattern = $log.matched_pattern
if ($pattern) {
if (-not $ruleStats[$ruleName].Patterns.ContainsKey($pattern)) {
$ruleStats[$ruleName].Patterns[$pattern] = 0
}
$ruleStats[$ruleName].Patterns[$pattern]++
}
}
# 计算成功率
foreach ($ruleName in $ruleStats.Keys) {
$stats = $ruleStats[$ruleName]
$successRate = $stats.Success / $stats.Hits
$ruleStats[$ruleName]['SuccessRate'] = [math]::Round($successRate, 2)
}
return $ruleStats
}PowerShell规则优化
function Optimize-RoutingRules {
param(
[hashtable]$RuleStats,
[string]$ConfigPath = "C:\Users\Administrator\.openclaw\workspace-main\skills\agent-dispatch\config.json"
)
# 加载当前配置
$config = Get-Content $ConfigPath -Raw | ConvertFrom-Json
$currentRules = $config.routing
$optimizedRules = @()
foreach ($rule in $currentRules) {
$ruleName = $rule.target
$stats = $RuleStats[$ruleName]
if (-not $stats) {
# 没有统计数据，保持不变
$optimizedRules += $rule
continue
}
# 根据命中率优化
if ($stats.Hits -gt 50 -and $stats.SuccessRate -gt 0.95) {
# 高命中率规则，考虑添加更多关键词
Write-Host "Rule '$ruleName' has high hit rate ( $($stats.Hits) hits, $($stats.SuccessRate) success rate)" -ForegroundColor Green
Write-Host " Top patterns: $($stats.Patterns.Keys | Sort-Object value -Descending | Select-Object -First 3)"
# 保持原规则
$optimizedRules += $rule
} elseif ($stats.Hits -lt 10) {
# 低命中率规则，考虑移除或降低优先级
Write-Host "Rule '$ruleName' has low hit rate ( $($stats.Hits) hits)" -ForegroundColor Yellow
Write-Host " Consider removing or lowering priority"
# 保持原规则（需要人工决策）
$optimizedRules += $rule
} elseif ($stats.SuccessRate -lt 0.85) {
# 低成功率规则，需要优化pattern
Write-Host "Rule '$ruleName' has low success rate ( $($stats.SuccessRate) )" -ForegroundColor Red
Write-Host " Consider optimizing pattern: $($rule.pattern)"
# 保持原规则（需要人工决策）
$optimizedRules += $rule
} else {
# 正常规则，保持不变
$optimizedRules += $rule
}
}
# 返回优化建议
return @{
OptimizedRules = $optimizedRules
Suggestions = Generate-OptimizationSuggestions -RuleStats $RuleStats
}
}
function Generate-OptimizationSuggestions {
param(
[hashtable]$RuleStats
)
$suggestions = @()
foreach ($ruleName in $RuleStats.Keys) {
$stats = $RuleStats[$ruleName]
# 建议添加的关键词
if ($stats.Hits -gt 50) {
$topPatterns = $stats.Patterns.Keys | Sort-Object value -Descending | Select-Object -First 5
$suggestions += @{
Rule = $ruleName
Type = "add_keywords"
Reason = "High hit rate"
Keywords = $topPatterns
}
}
# 建议移除的规则
if ($stats.Hits -lt 5 -and $stats.SuccessRate -lt 0.5) {
$suggestions += @{
Rule = $ruleName
Type = "remove_rule"
Reason = "Very low hit rate and success rate"
}
}
# 建议优化pattern
if ($stats.SuccessRate -lt 0.85 -and $stats.Hits -gt 10) {
$suggestions += @{
Rule = $ruleName
Type = "optimize_pattern"
Reason = "Low success rate despite decent hit rate"
}
}
}
return $suggestions
}📊 优化效果评估
评估指标
metrics:
- name: "routing_accuracy"
description: "Routing规则准确率"
formula: "正确路由数 / 总路由数"
target: "> 0.9"
- name: "routing_coverage"
description: "Routing规则覆盖率"
formula: "被路由的任务数 / 总任务数"
target: "> 0.95"
- name: "avg_routing_time"
description: "平均路由耗时"
formula: "总路由时间 / 路由次数"
target: "< 2s"
- name: "user_satisfaction"
description: "用户满意度"
formula: "满意的路由 / 总路由"
target: "> 0.9"🎓 使用示例
示例1: 基础优化
# 加载调度日志
dispatch_logs = load_dispatch_logs(days=7)
# 分析命中率
hit_rate = analyze_hit_rate(dispatch_logs)
# 分析准确率
accuracy = analyze_routing_accuracy(dispatch_logs)
# 生成优化建议
suggestions = generate_optimization_suggestions(hit_rate, accuracy)
# 输出报告
print("Routing优化报告:")
for rule_name, stats in hit_rate.items():
print(f"\
{rule_name}:")
print(f" 命中率: {stats['hits']} 次")
print(f" 成功率: {stats['success_rate']:.2%}")示例2: A/B测试
# 定义两个routing策略
routing_a = load_routing_config("config-v1.json")
routing_b = load_routing_config("config-v2.json")
# 加载测试查询
test_queries = load_test_queries()
# 运行A/B测试
results = ab_test_routing(routing_a, routing_b, test_queries)
# 输出结果
print(f"策略A准确率: {results['strategy_a']['avg_accuracy']:.2%}")
print(f"策略B准确率: {results['strategy_b']['avg_accuracy']:.2%}")
print(f"优胜者: 策略{results['winner']}")⚙️ 配置文件
routing-optimization-config.json
{
"version": "1.0",
"config": {
"data_collection_days": 7,
"optimization_interval": 604800,
"enable_ab_testing": true,
"enable_auto_optimization": false
},
"optimization_strategies": {
"hit_rate_analysis": {
"enabled": true,
"high_hit_threshold": 50,
"low_hit_threshold": 10
},
"accuracy_analysis": {
"enabled": true,
"min_accuracy": 0.85
},
"ab_testing": {
"enabled": true,
"sample_size": 100,
"min_confidence": 0.95
},
"dynamic_priority": {
"enabled": false,
"adjustment_interval": 86400
}
},
"keywords": {
"coder": [
"debug", "code", "github", "pr", "git", "api",
"编程", "代码", "调试", "开发"
],
"danao": [
"search", "research", "study", "learn", "总结",
"搜索", "研究", "学习", "调研"
],
"writer": [
"write", "article", "content", "公众号", "小红书",
"写作", "文章", "文案", "创作"
],
"engineer": [
"design", "architecture", "方案", "系统",
"设计", "架构", "技术方案"
],
"manager": [
"schedule", "task", "manage", "飞书", "日程",
"日程", "任务", "管理", "协作"
]
}
}🚀 未来优化
短期 (1-2周)
- [ ] 添加更多中文关键词
- [ ] 实现自动化A/B测试
- [ ] 添加用户反馈收集
中期 (1个月)
- [ ] 实现机器学习优化routing
- [ ] 添加上下文感知routing
- [ ] 实现个性化routing规则
长期 (3个月)
- [ ] 引入强化学习优化策略
- [ ] 实现自适应routing调整
- [ ] 构建routing知识库
*Skill版本: v1.0* *最后更新: 2026-03-26* *维护者: 象腿 (main agent)*