Check GitHub bounty issues before an AI coding agent spends time on them. The read-only scanner can verify whether a specific issue has on-chain escrow backing and classify bounties as backed, unbacked, or quarantined; it also exposes open verified bounties and mining pool telemetry.
Pre-flight GitHub bounty security scanner and on-chain Proof-of-Escrow MCP verifier for AI coding agents. Classifies GitHub bounties into SAFE_WITH_ESCROW, SAFE_NO_ESCROW, or QUARANTINE.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
BountyGuard is a read-only pre-flight security audit tool and Model Context Protocol (MCP) server that evaluates open GitHub bounty issues before an AI agent invests compute.
list_escrows: Lists all open verified Proof-of-Escrow GitHub bounties backed by locked on-chain reserves.verify_issue_escrow: Verifies on-chain escrow backing for a specific GitHub issue (owner/repo#number).get_pool_leaderboard: Returns live Proof-of-Work mining pool leaderboard telemetry.Run the built-in Python module on any repository issue identifier:
python3 -m bountyguard check owner/repo#1
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.
Turn a winning TikTok or Meta ad into a structured creative test plan. Identify what made it work, plan distinct concepts, set test budgets and review timing, and use funnel metrics to decide whether to stop, iterate, or scale each ad.
Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.
Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.
Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.
Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.