Algora Bounty Radar

Find public open-source bounty leads worth reviewing before you spend implementation time.

Algora Bounty Radar is a read-only Apify Actor that searches public GitHub and Algora-style bounty signals, then scores each candidate for funding evidence, payout-route confidence, competition, safety risk, and implementation clarity.

The Actor is built for operators, contributors, and agencies that want a smaller, safer review queue instead of a long list of bounty-looking issues.

What It Does

• Searches public GitHub issue data with bounty-focused queries.
• Detects public Algora-style funding signals and explicit USD/USDC amounts.
• Scores candidates by funding proof, payout route, acceptance criteria, freshness, competition, and safety.
• Separates results into REVIEW, MONITOR, and REJECT.
• Writes normalized rows to the default Apify dataset.
• Writes a compact run summary to the default key-value store output.

What It Does Not Do

• It does not claim bounties.
• It does not submit pull requests.
• It does not comment on GitHub issues.
• It does not guarantee payment, acceptance, or bounty wins.
• It does not configure wallets, Stripe, tax, or payout accounts.
• It does not use private repos, private mailbox data, cookies, or session data.

Best Use Cases

• Building a shortlist of funded OSS issues for manual review.
• Filtering noisy GitHub reward text before assigning engineering time.
• Monitoring whether public bounty leads have enough evidence to justify deeper review.
• Auditing why a high-dollar issue is still too risky or too vague to pursue.

Input

The main inputs are:

• queries: GitHub search queries. Keep these narrow and bounty-specific.
• rails: preferred evidence rails, such as algora and github_text_seed.
• minAmountUsd: minimum clear bounty amount.
• maxOpenPrs: maximum visible open pull requests before a candidate is rejected.
• maxClaimCount: maximum claim-like comment count before a candidate is rejected.
• maxResults: maximum rows to return.
• rejectUnsafePatterns: rejects prompt disclosure, fake engagement, social gates, wallet-funding traps, and unsafe production testing.
• dryRun: uses provided seed issues or bundled examples and skips live GitHub search.
• seedIssues: optional issue records for controlled testing.

Example:

{
  "queries": [
    "algora bounty is:issue is:open",
    "\"$\" \"bounty\" is:issue is:open"
  ],
  "minAmountUsd": 25,
  "maxOpenPrs": 2,
  "maxClaimCount": 2,
  "maxResults": 25,
  "rejectUnsafePatterns": true,
  "dryRun": false
}

Output

Each dataset row represents one scored candidate.

Important fields:

• decision: REVIEW, MONITOR, or REJECT.
• score: numeric confidence score.
• rail: detected evidence rail.
• funding_proof: funding evidence class, such as public_bounty_page or text_only.
• payout_route: expected payout route when it can be inferred from public evidence.
• amount and currency: detected bounty amount.
• repo, issue_number, title, issue_url, and bounty_url.
• risk_flags: unsafe or low-trust patterns found in the issue text.
• reject_reasons: concrete reasons the candidate should not receive implementation time.
• next_action: manual next step recommendation.

Example row:

{
  "decision": "REVIEW",
  "score": 84,
  "rail": "algora",
  "funding_proof": "public_bounty_page",
  "payout_route": "stripe_connect",
  "amount": 2500,
  "currency": "USD",
  "repo": "algora-io/algora",
  "issue_number": 238,
  "title": "[UI Bug] Unauthorized 'Edit' and 'Delete' buttons visible on /bounties page",
  "issue_url": "https://github.com/algora-io/algora/issues/238",
  "risk_flags": [],
  "reject_reasons": [],
  "next_action": "Manual review before any patch-prep packet. This Actor does not claim, comment, or create PRs."
}

Decision Labels

REVIEW means the candidate has enough public signal for manual due diligence. It is not a patch instruction.

MONITOR means the candidate has some useful signal but needs better funding, acceptance, or competition evidence.

REJECT means the Actor found a concrete reason to avoid spending implementation time.

Data Sources

This Actor uses public GitHub issue search data and public issue text. It may infer Algora-style evidence from public references to Algora, bounty pages, or bounty amounts. Inferred payout routes are directional signals, not payout guarantees.

Operational Notes

Use dryRun: true for private tests and controlled demos. Use low maxResults values for first live runs. Review REVIEW rows manually before starting any implementation work.

For public Store use, pricing charges for filtering and scoring value, not for bounty outcomes. The intended pay-per-event event is scored-candidate, charged once per scored dataset row when the Actor is configured as a paid Apify Actor.