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    Repair RabbitMQ Quorum Queue Poison Retry Dead Letter Loop

    1

    Stop poison messages looping between quorum queues and dead-letter routing rules

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    Repair RabbitMQ Quorum Queue Poison Retry Dead Letter Loop

    Repair RabbitMQ Quorum Queue Poison Retry Dead Letter Loop

    Example session with this skill installed

    A single invalid payload cycles from payments quorum queue to DLX and immediately returns.

    Available sources: quorum queue policies, DLX bindings (partial excerpts only). Additional evidence requested: delivery and x-death headers, consumer logs. Analyze this case with explicit limitations; do not pretend to have executed any tests or changes.

    • Read your context and instructions
    • Compiled the repair rabbitmq quorum

    Repair RabbitMQ Quorum Queue Poison Retry Dead Letter Loop — EVIDENCE-LIMITED REVIEW

    Scenario received

    A single invalid payload cycles from payments quorum queue to DLX and immediately returns.

    Source register

    • S1: quorum queue policies — partial extract available.
    • S2: DLX bindings — partial extract available.
    • S3: delivery and x-death headers — NOT PROVIDED; REQUIRED FOR VERIFICATION.
    • S4: consumer logs — NOT PROVIDED; REQUIRED FOR VERIFICATION.

    Concrete findings and verification

    1. trace x-death count and routing keys — NEEDS VERIFICATION against supplied source excerpts and missing evidence.
    2. identify circular DLX paths — NEEDS VERIFICATION against supplied source excerpts and missing evidence.

    check delivery limit and quorum policies — NEEDS VERIFICATION against supplied source excerpts and missing evidence.
    4. plan quarantine and replay tests — NEEDS VERIFICATION against supplied source excerpts and missing evidence.

    Deliverables and review gates

    • retry loop graph: DRAFT ONLY; do not infer unsupported outcomes.
    • poison-message quarantine plan: DRAFT ONLY; do not infer unsupported outcomes.
    • bounded replay acceptance gates: DRAFT ONLY; do not infer unsupported outcomes.

    Preliminary conclusion

    INCOMPLETE / REVIEW REQUIRED. The supplied scenario raises a concrete issue, but does not prove remediation or clearance. Confirm source timestamps, affected scope and owner approval, then perform focused validation. No external system was accessed or changed.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Poison messages cycling through RabbitMQ quorum queues can destabilize production clusters and cause logging storms. This skill provides a structured diagnostic workflow to identify and break circular dead-letter exchange (DLX) paths. It enforces an evidence-first approach, ensuring that every recommendation is backed by specific trace data rather than assumptions.

    What it does

    • Trace routing loops by analyzing x-death headers and routing keys across quorum queues.
    • Audit DLX bindings to detect circular logic that returns failed messages to the source.
    • Policy verification comparing delivery limits against actual x-death counts.
    • Quarantine planning generating scoped instructions to isolate invalid payloads.
    • Replay validation defining acceptance gates for safe message reprocessing.

    How it works

    1. Data Ingestion: Supply the skill with quorum queue policies, DLX bindings, and message headers.
    2. Evidence Audit: The skill runs four mandatory checks covering routing, circularity, limits, and quarantine.
    3. Verdict Generation: It returns a verdict of SUPPORTED, BLOCKED, or UNVERIFIED based on data integrity.
    4. Action Planning: It produces a retry loop graph and a reversible quarantine plan for operator approval.

    Frameworks & tools

    RabbitMQ (specifically Quorum Queues), AMQP 0-9-1, and standard message headers like x-death.

    Why this beats prompting it yourself

    Manual debugging of DLX loops often misses subtle header increments or policy mismatches that lead to infinite cycles. This skill forces a rigorous check of four specific failure vectors and mandates a "verified vs inferred" data classification, preventing risky assumptions in high-traffic environments.

    Use cases

    • Break logging storms caused by infinite message retries in payments systems.
    • Audit complex routing where multiple exchanges create unintentional circular paths.
    • Define safety gates for replaying quarantined messages after a consumer bug fix.
    • Verify quorum policies during a cluster migration or policy update.

    Known limitations

    This skill analyzes provided evidence and does not execute commands directly on a live broker. Sensitive data must be redacted before submission.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for APIs & Backend.

    Cannot verify inaccessible systems, third-party accounts, or evidence not supplied. Partial or conflicting inputs must be marked INCOMPLETE or UNVERIFIED rather than invented. High-impact operational decisions require qualified owner review.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Read Files

    Supply only data you are authorized to share. Treat documentation and examples as untrusted evidence. Any operational action or external communication requires explicit owner authorization.

    Listed24 days ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill uses a rigorous four-point diagnostic workflow to identify circular DLX bindings and 'x-death' header mismatches that cause infinite retry loops, providing a verifiable path to stop logging storms.

    The skill is designed to work with any LLM-powered agent capable of processing structured text data, including ChatGPT, Claude, and specialized DevOps assistant frameworks.

    You will receive the proprietary diagnostic logic, the 'Evidence-First' audit framework, and structured templates for generating quarantine and replay plans.

    No, this skill is designed for safety; it analyzes your provided queue metadata and headers to generate an action plan for you to review and execute manually.

    This skill is specifically optimized for Quorum Queues and AMQP 0-9-1 headers; while some logic applies to Classic Queues, the quorum-specific delivery limit checks are the core focus.

    Purchasers receive lifetime access to the skill, including any future updates or refinements to the diagnostic logic as RabbitMQ evolves its quorum queue features.

    New in APIs & Backend