Bounty Hunter Agent

Metadata

• Name: bounty-hunter-agent
• Version: 1.0.0
• Author: lanxevo3
• Tags: github, bounty, automation, monetization, algora, opire
• License: MIT

Description

Autonomous GitHub bounty hunting agent. Scans for paid issues across GitHub, Algora, and Opire, evaluates viability based on competition level and payout amount, and helps you prioritize and submit PRs automatically.

What It Does

1. Scans repositories for bounty-labeled issues (bounty, 💰, reward, paid, algora, opire)
2. Evaluates competition level by checking existing PRs and comments on each issue
3. Scores opportunities by payout amount, competition density, issue age, and repo activity
4. Prioritizes a ranked list of actionable bounties sorted by expected value
5. Tracks state in a local JSON file so you never re-scan the same issues
6. Spawns fix sessions — integrates with OpenClaw to kick off autonomous coding sessions for top-ranked bounties

Prerequisites

• gh CLI authenticated (gh auth status should succeed)
• Python 3.8+ (stdlib only — no pip dependencies)
• Optional: OpenClaw runtime for automated fix session spawning

Usage

Quick Scan

python ~/.agents/skills/bounty-hunter-agent/scripts/bounty_scan.py

With OpenClaw

When installed as a skill, invoke via:

/bounty-hunter-agent scan

The agent will:

• Search GitHub for bounty-labeled issues
• Check Algora and Opire for listed bounties
• Output a ranked JSON report to ~/.agents/skills/bounty-hunter-agent/state/bounties.json
• Print a human-readable summary to stdout

Configuration

Set environment variables to customize behavior:

Output Format

The scan produces a ranked list:

[
  {
    "rank": 1,
    "score": 87.5,
    "repo": "org/repo",
    "issue": 123,
    "title": "Add feature X",
    "payout_usd": 200,
    "competing_prs": 1,
    "url": "https://github.com/org/repo/issues/123",
    "labels": ["bounty", "💰 200"],
    "age_days": 3
  }
]

How Scoring Works

score = payout_weight * (payout / max_payout)
      + competition_weight * (1 - competing_prs / max_competition)
      + freshness_weight * (1 - age_days / max_age)

Default weights: payout=50, competition=35, freshness=15

Lower competition + higher payout + newer issue = higher score.