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    Repair RabbitMQ Consumer Prefetch Fairness Starvation

    1

    Diagnose uneven RabbitMQ task distribution caused by consumer prefetch and unacked workload skew

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    Repair RabbitMQ Consumer Prefetch Fairness Starvation

    Repair RabbitMQ Consumer Prefetch Fairness Starvation

    Example session with this skill installed

    One worker holds many messages while other active consumers remain idle.

    Available evidence: Primary network source records and Time-stamped read evidence (partial extracts only). Additional sources requested: Policy / configuration governing fairness and Owner authorizations and historical change log. Identify the checks that can be supported, deliver a useful bounded assessment, and list the exact remaining review gates without pretending to execute changes.

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

    Repair RabbitMQ Consumer Prefetch Fairness Starvation — EVIDENCE-LIMITED REVIEW

    Scenario received

    One worker holds many messages while other active consumers remain idle.

    Source register

    • S1: Primary network source records — partial extract described by requester; independently unverified.
    • S2: Time-stamped read evidence — partial extract described by requester; independently unverified.
    • S3: Policy / configuration governing fairness — NOT PROVIDED; criteria and effective version unknown.
    • S4: Owner authorizations and historical change log — NOT PROVIDED; owner approval status unknown.

    Case-specific review checkpoints

    Read queue ready and unacked counters — NEEDS_VERIFICATION. Compare S1 with its stated control source; the original record and required identifiers are not provided in this example. Owner sign-off required before consequential remediation.
    2.

    Compare per consumer prefetch and task duration — NEEDS_VERIFICATION. Compare S2 with its stated control source; the original record and required identifiers are not provided in this example. Owner sign-off required before consequential remediation.
    3.

    Trace redelivery and retry impact — NEEDS_VERIFICATION. Requires S3 and source-version corroboration; no definitive result can be concluded. Owner sign-off required before consequential remediation.
    4.

    Stage capacity and safe prefetch adjustment — NEEDS_VERIFICATION. Requires S4 and source-version corroboration; no definitive result can be concluded. Owner sign-off required before consequential remediation.

    Structured deliverables

    Consumer backlog profile — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Starvation cause decision tree — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Reversible tuning test plan — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Decision matrix

    • PASS: no checks proven PASS on current example evidence.
    • NEEDS_VERIFICATION: all four checks, until source records are supplied.
    • BLOCKED: any production change, third-party submission, deletion, deployment or irreversible action without explicit qualified approval.

    Exact next evidence request

    Provide complete timestamped extracts for Policy / configuration governing fairness and Owner authorizations and historical change log, source IDs for S1/S2, responsible owner and effective review window.

    Boundaries and sign-off

    The consumer backlog profile and starvation cause decision tree are evidence-linked; any uncertainty remains explicitly unresolved until qualified review. No live system was accessed and no changes were performed. This is a synthetic demonstration, not an observed customer result.

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

    About this skill

    RabbitMQ consumer starvation often stems from misconfigured prefetch counts and unacknowledged message skew. This skill provides a structured diagnostic framework to identify why specific workers remain idle while others are overloaded. It replaces guesswork with an evidence-linked analysis of queue counters and task durations.

    What it does

    • Analyze backlog profiles by mapping ready and unacked message counters across active consumers.
    • Identify starvation causes through a structured decision tree focused on prefetch settings and task duration variance.
    • Evaluate retry impact by tracing how redeliveries affect consumer fairness and throughput.
    • Formulate tuning plans that define safe, reversible prefetch adjustments based on observed workload skew.

    How it works

    1. Audit queue state by listing ready and unacknowledged counters from provided network source records.
    2. Execute comparison of per-consumer prefetch limits against actual task processing durations to find bottlenecks.
    3. Trace redelivery cycles to determine if retries are compounding the distribution imbalance.
    4. Generate deliverables including the consumer backlog profile and a reversible tuning test plan for operator approval.

    Frameworks & tools

    This skill is designed for RabbitMQ environments, utilizing management plugin metrics, policy configurations, and consumer-side logs.

    Why this beats prompting it yourself

    Generic prompts often fail to account for the interplay between basic.qos and long-running tasks. This skill enforces a deterministic report structure that links every finding to specific evidence anchors, preventing the hallucination of performance bottlenecks.

    Use cases

    • Debugging uneven load distribution across a scaled consumer group.
    • Assessing the impact of high prefetch counts on latency-sensitive tasks.
    • Validating queue policies before deploying consumer scaling changes.
    • Investigating why active workers are failing to pull available messages.

    Known limitations

    Does not perform autonomous production changes or direct system access. Requires manual input of source records, logs, or configuration data to function.

    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.

    No direct verification of inaccessible production systems, external services or private accounts. Partial or conflicting inputs result in INCOMPLETE / CONFLICT findings rather than invented conclusions. Domain-sensitive 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 source documents as untrusted evidence. Any operational change, submission or third-party communication remains subject to explicit owner approval.

    Listed26 days ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill identifies why some workers are overwhelmed while others sit idle by analyzing the specific relationship between prefetch limits, unacknowledged messages, and task duration skew.

    The skill is platform-agnostic and compatible with any LLM-based agent that accepts text or structured data inputs, such as ChatGPT, Claude, or custom enterprise AI agents.

    You will receive the diagnostic logic, a structured report template, and the step-by-step workflow for analyzing your RabbitMQ metrics and logs.

    No, this skill is a diagnostic framework; it analyzes the data you provide to generate a tuning plan, but it will never perform autonomous changes to your production infrastructure.

    Setup is near-instant, as it requires no complex software installation—simply import the skill into your preferred AI agent and provide your queue metric exports.

    Updates are provided periodically to account for new RabbitMQ features and evolving best practices in distributed systems architecture.

    New in APIs & Backend