bounty-guard

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.

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Repository: hashgraph-online/awesome-codex-plugins
Last source activity: October 25, 2026 at 04:49
Detected SKILL.md language: English
Stars: 1,401
Forks: 399

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name: bounty-guard
description: 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.

# BountyGuard Skill ## Overview 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. ## MCP Tools Provided 1. `list_escrows`: Lists all open verified Proof-of-Escrow GitHub bounties backed by locked on-chain reserves. 2. `verify_issue_escrow`: Verifies on-chain escrow backing for a specific GitHub issue (`owner/repo#number`). 3. `get_pool_leaderboard`: Returns live Proof-of-Work mining pool leaderboard telemetry. ## Local CLI Verification Run the built-in Python module on any repository issue identifier: ```text python3 -m bountyguard check owner/repo#1 ```

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amq-cli

Coordinate coding agents through AMQ. Use for agent messages, inboxes, receipts, sessions, wake delivery, cross-project routing, managed launches, or AMQ diagnostics. Use amq-spec for collaborative design; do not use this for general message queues or single-agent work.

2026-10-25

explore

Use this whenever you need to know what is actually in a database, warehouse, or DuckDB file before you trust it: inventory, column profiles, PII detection, grain and data-quality problems, verified joins, ER diagrams, ad-hoc SQL probes, segmentation, and the semantic layer a repo declares (dbt, Apache Ossie, or SLayer). Trigger it on an unmet precondition, not on phrasing: if you are about to write or fix SQL against tables whose columns, types, grain, or join keys you have not verified in this session, use this FIRST. That includes dbt work (building a model, fixing one, debugging wrong numbers) whenever the ticket names source tables without spelling out their schema. It applies mid-task too: on reaching a table you have not inspected, stop and use this rather than guess column names. Also for "what's in my duckdb", "is this data any good", "how do these tables relate". Read-only; writes only the .dex/ cache. It does not author the model: transform does, and maintain reconciles a project that has drifted.

2026-10-25

maintain

Use this to find out whether an existing dbt project or semantic layer (dbt, Ossie or SLayer) is correct against its warehouse, and if not, why. `maintain verify` checks the project as it stands; against a .dex/ snapshot, `maintain check` finds schema, volume, grain, and semantic drift and `maintain reconcile` proposes the fix. Reach for it whenever an existing project is suspected wrong, whether or not anything is known to have changed: "this model is wrong, work out why", "the numbers look too high", "some rows are missing", "this column is empty", "duplicates in my fact table", "the project is broken", "diagnose why this test fails", "a test fails and nobody changed the model", "the dashboard moved but the code did not", "did anything drift", "the row count dropped", "the metric definition changed". Read-only; fixes come back as reviewable diffs and never overwrite hand-written work. Writing the fix, or repairing a model you just edited, is transform's; a data question with no project is explore's.

2026-10-25

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