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
- Scans repositories for bounty-labeled issues (
bounty,💰,reward,paid,algora,opire) - Evaluates competition level by checking existing PRs and comments on each issue
- Scores opportunities by payout amount, competition density, issue age, and repo activity
- Prioritizes a ranked list of actionable bounties sorted by expected value
- Tracks state in a local JSON file so you never re-scan the same issues
- Spawns fix sessions — integrates with OpenClaw to kick off autonomous coding sessions for top-ranked bounties
Prerequisites
- gh CLI authenticated (
gh auth statusshould 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.pyWith OpenClaw
When installed as a skill, invoke via:
/bounty-hunter-agent scanThe 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:
| Variable | Default | Description |
|---|---|---|
BOUNTY_MIN_PAYOUT | 25 | Minimum payout in USD to consider |
BOUNTY_MAX_COMPETITION | 5 | Max competing PRs before skipping |
BOUNTY_SCAN_LIMIT | 100 | Max issues to scan per query |
BOUNTY_STATE_DIR | ~/.agents/skills/bounty-hunter-agent/state | Where to store state |
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.