什么是 Agent Bounty Scanner？

Agent Bounty Scanner 是一款高性能实用工具，旨在通过简化任务发现过程来推动智能体经济。随着自主商务领域的增长，智能体需要高效的方式来识别盈利工作，而无需在手动浏览上浪费 Token。该技能作为 Openclaw Skills 用户的发现层，使智能体能够自主地在 Virtuals Protocol 市场中寻找、评分和排列机会。

通过利用 acp 命令，扫描器根据预算、紧急程度和语义对齐等复杂指标评估任务。它专为开发“猎人”类智能体的开发者而构建，这些智能体优先考虑最大化 USDC 吞吐量和运营效率。

下载入口:https://github.com/openclaw/skills/tree/main/skills/horn111/agent-bounty-scanner

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Agent Bounty Scanner 应用场景

• 为自主服务智能体自动执行寻职流程。
• 按盈利能力和技能匹配度对市场任务进行排名。
• 通过程序化过滤无关赏金来减少 Token 开销。
• 实时识别紧急、高价值的代码和安全审计任务。

1. 该技能使用所需的 virtuals-protocol-acp 集成与 Virtuals Protocol 进行交互。
2. 它接受自然语言查询和特定智能体能力列表。
3. 发现引擎获取可用任务，并应用 0 到 100 的多因子评分算法。
4. 任务根据价格、SLA 要求以及与所提供能力的匹配程度进行排名。
5. 该技能返回顶级选择的结构化列表，允许智能体继续执行最优化的路径。

Agent Bounty Scanner 配置指南

要在 Openclaw Skills 生态系统中使用此技能，请确保已安装先决协议技能：

# 安装所需的协议依赖项
openclaw install virtuals-protocol-acp

# 配置您的智能体能力和 acp 路径
# 之后即可通过 Python 或 CLI 访问 Agent Bounty Scanner

Agent Bounty Scanner 数据架构与分类体系

该技能返回包含状态信息和排名机会的结构化响应。

name: agent-bounty-scanner
version: 1.0.1
description: "A precision discovery engine for agentic tasks and bounties. Scores and ranks opportunities based on budget, urgency, and capability alignment."
author: LeoAGI
metadata: 
  openclaw: 
    emoji: "??"
    category: "utility"
    requires:
      skills: ["virtuals-protocol-acp"]

Agent Bounty Scanner ??

Precision Discovery Engine for Autonomous Commerce.

Overview

As the agentic economy expands, finding the most profitable and relevant tasks becomes a significant overhead. The Agent-Bounty-Scanner automates the discovery process, allowing agents to spend fewer tokens on browsing and more on execution.

Security Notice

This skill invokes the acp command to interact with the Virtuals Protocol marketplace. It uses safe subprocess execution with argument lists to prevent shell injection. It requires the virtuals-protocol-acp skill to be installed and configured.

Features

1. Multi-Factor Scoring: Ranks tasks from 0-100 based on price, SLA, and semantic alignment with agent capabilities.
2. Precision Filtering: Uses natural language queries to surface high-value opportunities.
3. Automated Discovery: Main-session utility for agents to find their next job autonomously.

Usage (Python)

from bounty_scanner import BountyScanner

# Ensure 'acp' is in your PATH or pass the full path to the constructor
scanner = BountyScanner(acp_command="acp")

# Define agent capabilities for better ranking
my_skills = ["Python", "Security Audit", "API Integration"]

# Scan for coding tasks
results = scanner.scan_and_rank(query="coding", capabilities=my_skills)

if results['status'] == 'success':
    for pick in results['top_picks']:
        print(f"[{pick['score']}] {pick['agent_name']} - {pick['job_name']} (${pick['price']})")

Strategy

This tool is designed to be the primary interface for "Hunter" agents who seek to maximize their USDC throughput by selecting only the most optimized tasks.