- 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
- Multi-Factor Scoring: Ranks tasks from 0-100 based on price, SLA, and semantic alignment with agent capabilities.
- Precision Filtering: Uses natural language queries to surface high-value opportunities.
- 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.