Agent Optimizer.Skip

V6.1 Agent 性能优化器 - 基于轨迹分析和奖励反馈的轻量级优化框架

Security Scan

The skill's files, instructions, and requirements are coherent with an on‑agent performance optimizer: it only reads/writes workspace files and runs local Python scripts, but you should review data retention and access because it records full inputs/outputs.

Purpose & Capability

Name/description (Agent performance optimizer) match the requested binary (python3), the included scripts, and the SKILL.md instructions. All declared requirements are proportionate to the stated purpose.

Instruction Scope

Instructions direct the agent to record full trajectories (inputs, outputs, tool calls, timing) into /workspace/subagents/{agent_id}/optimizer and to read those files for analysis—this is expected for an optimizer but can capture sensitive data (prompts, outputs, user data). There are no external network calls or instructions to read unrelated system files.

Install Mechanism

No install spec; this is an instruction-only skill with two small local Python scripts. Nothing is downloaded or extracted from external URLs.

Credentials

The skill requests no environment variables or credentials. It operates on workspace paths only, which is proportionate to its purpose.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request elevated privileges or modify other skills' configs; it writes only to its own optimizer directory under the agent workspace.

Assessment

This skill appears to do what it says: local trajectory recording, reward logging, and simple analysis using Python. Before installing, review and decide whether storing full inputs/outputs in /workspace/subagents/{agent_id}/optimizer is acceptable (these files can contain prompts, model outputs, or user data). Consider: (1) restricting who/what can read those files, (2) adding retention, redaction or encryption policies, (3) testing the scripts in a safe environment (they are local and have no network calls), and (4) fixing a minor bug in generate_report.py (it uses defaultdict but does not import it). If you need strict data isolation, do not enable this skill until you add access controls or modify it to redact secrets.

Agent Optimizer ⚡

V6.1 联邦智能专用 - 轻量级 Agent 性能优化框架

无需外部依赖，基于 OpenClaw 原生能力实现 Agent 性能持续优化。

🔥 核心功能

1. 轨迹记录

• 自动记录 Agent 执行轨迹
• 保存输入、输出、工具调用、耗时
• 结构化存储便于分析

2. 奖励反馈

• 支持多种奖励信号（用户评分、任务完成度、ROI 等）
• 累积奖励统计
• 奖励趋势分析

3. 提示词优化

• 基于奖励反馈自动优化提示词
• A/B 测试不同提示词版本
• 保留历史版本可回滚

4. 性能分析

• 执行耗时分析
• 成功率统计
• ROI 计算与追踪

📦 安装

无需安装，已集成到 OpenClaw V6.1 工作区。

🚀 快速开始

1. 初始化优化器

# 在子 Agent 工作区创建 optimizer 目录
mkdir -p /workspace/subagents/{agent_id}/optimizer

# 创建配置文件
cat > /workspace/subagents/{agent_id}/optimizer/config.json << 'EOF'
{
  "agent_id": "techbot",
  "optimization_target": "tutorial_quality",
  "metrics": ["user_rating", "completion_rate", "roi"],
  "ab_test": true
}
EOF

2. 记录执行轨迹

import json
from datetime import datetime

def record_trajectory(agent_id, task, output, metrics):
    """记录 Agent 执行轨迹"""
    timestamp = datetime.now().isoformat()
    
    trajectory = {
        "agent_id": agent_id,
        "timestamp": timestamp,
        "task": task,
        "output": output,
        "metrics": metrics,
        "prompt_version": get_current_prompt_version()
    }
    
    # 保存到轨迹文件
    with open(f'/workspace/subagents/{agent_id}/optimizer/trajectories.jsonl', 'a') as f:
        f.write(json.dumps(trajectory) + '\n')
    
    return trajectory

3. 发射奖励信号

def emit_reward(agent_id, trajectory_id, reward_value, reward_type="user_rating"):
    """发射奖励信号"""
    timestamp = datetime.now().isoformat()
    
    reward = {
        "agent_id": agent_id,
        "trajectory_id": trajectory_id,
        "timestamp": timestamp,
        "reward_value": reward_value,
        "reward_type": reward_type
    }
    
    # 保存到奖励文件
    with open(f'/workspace/subagents/{agent_id}/optimizer/rewards.jsonl', 'a') as f:
        f.write(json.dumps(reward) + '\n')
    
    return reward

4. 分析性能并优化

def analyze_and_optimize(agent_id):
    """分析性能并生成优化建议"""
    import json
    
    # 加载轨迹数据
    trajectories = []
    with open(f'/workspace/subagents/{agent_id}/optimizer/trajectories.jsonl', 'r') as f:
        for line in f:
            trajectories.append(json.loads(line))
    
    # 加载奖励数据
    rewards = []
    with open(f'/workspace/subagents/{agent_id}/optimizer/rewards.jsonl', 'r') as f:
        for line in f:
            rewards.append(json.loads(line))
    
    # 计算平均奖励
    avg_reward = sum(r['reward_value'] for r in rewards) / len(rewards) if rewards else 0
    
    # 分析高奖励和低奖励轨迹
    high_reward_trajectories = [t for t in trajectories if get_reward(t['task'], rewards) > avg_reward]
    low_reward_trajectories = [t for t in trajectories if get_reward(t['task'], rewards) < avg_reward]
    
    # 生成优化建议
    optimization_report = {
        "agent_id": agent_id,
        "total_trajectories": len(trajectories),
        "total_rewards": len(rewards),
        "average_reward": avg_reward,
        "high_reward_patterns": analyze_patterns(high_reward_trajectories),
        "low_reward_patterns": analyze_patterns(low_reward_trajectories),
        "suggestions": generate_suggestions(high_reward_trajectories, low_reward_trajectories)
    }
    
    # 保存报告
    with open(f'/workspace/subagents/{agent_id}/optimizer/optimization_report.json', 'w') as f:
        json.dump(optimization_report, f, indent=2)
    
    return optimization_report

📊 使用场景

TechBot - 教程质量优化

# 记录教程生成轨迹
trajectory = record_trajectory(
    agent_id="techbot",
    task="编写 AI Agent 教程",
    output=tutorial_content,
    metrics={
        "word_count": len(tutorial_content),
        "code_blocks": count_code_blocks(tutorial_content),
        "execution_time": execution_time
    }
)

# 用户评分后发射奖励
emit_reward(
    agent_id="techbot",
    trajectory_id=trajectory['task'],
    reward_value=user_rating,  # 1-5 分
    reward_type="user_rating"
)

# 定期分析优化
report = analyze_and_optimize("techbot")
print(f"平均评分：{report['average_reward']:.2f}")
print(f"优化建议：{report['suggestions']}")

FinanceBot - ROI 预测优化

# 记录 ROI 预测轨迹
trajectory = record_trajectory(
    agent_id="financebot",
    task="预测任务 ROI",
    output={"predicted_roi": 2.5, "confidence": 0.85},
    metrics={
        "prediction_accuracy": 0.0,  # 待实际结果出来后更新
        "confidence_score": 0.85
    }
)

# 实际结果出来后发射奖励
actual_roi = 2.3
prediction_error = abs(2.5 - actual_roi)
reward = 1.0 / (1.0 + prediction_error)  # 误差越小奖励越高

emit_reward(
    agent_id="financebot",
    trajectory_id=trajectory['task'],
    reward_value=reward,
    reward_type="prediction_accuracy"
)

AutoBot - 抓取成功率优化

# 记录数据抓取轨迹
trajectory = record_trajectory(
    agent_id="autobot",
    task="抓取网站数据",
    output={"status": "success", "data_points": 150},
    metrics={
        "success": True,
        "data_points": 150,
        "retry_count": 0
    }
)

# 根据成功率发射奖励
reward = 1.0 if trajectory['output']['status'] == 'success' else 0.0
emit_reward(
    agent_id="autobot",
    trajectory_id=trajectory['task'],
    reward_value=reward,
    reward_type="success_rate"
)

📈 性能分析工具

1. 奖励趋势分析

# 生成奖励趋势图数据
python3 /workspace/skills/agent-optimizer/scripts/analyze_trends.py --agent techbot

2. A/B 测试

# 测试两个提示词版本
def ab_test_prompt(agent_id, task, version_a, version_b):
    # 随机选择版本
    import random
    version = random.choice(['a', 'b'])
    
    if version == 'a':
        output = execute_with_prompt(task, version_a)
    else:
        output = execute_with_prompt(task, version_b)
    
    # 记录并比较结果
    return output, version

3. 提示词版本管理

# 保存提示词版本
def save_prompt_version(agent_id, version, prompt_template):
    with open(f'/workspace/subagents/{agent_id}/optimizer/prompts/v{version}.txt', 'w') as f:
        f.write(prompt_template)

# 加载提示词版本
def load_prompt_version(agent_id, version):
    with open(f'/workspace/subagents/{agent_id}/optimizer/prompts/v{version}.txt', 'r') as f:
        return f.read()

🔧 配置文件示例

optimizer/config.json

{
  "agent_id": "techbot",
  "optimization_target": "tutorial_quality",
  "metrics": ["user_rating", "completion_rate", "roi"],
  "ab_test": true,
  "prompt_versions": ["v1.0", "v1.1", "v2.0"],
  "current_version": "v2.0",
  "optimization_interval": 100
}

optimizer/prompts/v2.0.txt

你是一个专业的技术教程作家。
请编写一个关于 {topic} 的教程。

要求：
1. 结构清晰，包含简介、步骤、示例代码
2. 代码可运行，有详细注释
3. 语言简洁，避免冗长
4. 包含实际应用场景

教程长度：{word_count} 字左右

📁 目录结构

/workspace/subagents/{agent_id}/optimizer/
├── config.json              # 优化配置
├── trajectories.jsonl       # 执行轨迹记录
├── rewards.jsonl            # 奖励信号记录
├── optimization_report.json # 优化分析报告
├── prompts/                 # 提示词版本目录
│   ├── v1.0.txt
│   ├── v1.1.txt
│   └── v2.0.txt
└── scripts/                 # 分析脚本
    ├── analyze_trends.py
    ├── ab_test.py
    └── generate_report.py

⚠️ 注意事项

1. 隐私保护: 轨迹数据可能包含敏感信息，注意脱敏
2. 存储管理: 定期清理旧轨迹数据，避免文件过大
3. 奖励设计: 奖励函数要合理，避免优化错方向
4. 版本控制: 提示词版本要记录清晰，方便回滚

🎯 与 Agent Lightning 对比

功能	Agent Lightning	Agent Optimizer
安装复杂度	需要 pip 安装	✅ 零安装
依赖	Python 包依赖	✅ 无依赖
RL 训练	✅ 完整 RL 支持	基础奖励反馈
提示词优化	✅ 自动优化	✅ 手动+A/B 测试
集成难度	中等	✅ 简单
适用场景	大规模训练	✅ 轻量级持续优化

🚀 未来扩展

• 自动化提示词优化算法
• 集成简单 RL 算法（如 Bandit）
• Web Dashboard 可视化
• 多 Agent 协同优化
• 奖励函数模板库

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Agent Optimizer - V6.1 原生性能优化框架