RobustMQ Metrics

SkillMonitoring & ops

Lets your agent design and add metrics and Grafana dashboards for RobustMQ services.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the RobustMQ Metrics skill

About this capability

Designs and implements minimal, high-value metrics for RobustMQ services and dashboards. Use when the user asks to add metrics, improve observability, or update Grafana panels for core processing pipelines.

What this skill tells your AI

The instructions your AI receives, as published by robustmq/robustmq in .claude/skills/robustmq-metrics/SKILL.md and read by ahel’s review.

Purpose

Add observability with minimal but complete coverage of a target pipeline:

  • process count and process duration
  • retry and terminal outcomes (if applicable)
  • key failure points in read/process/write path
  • liveness/health

Do not over-instrument. Prefer compact metrics that support operations and debugging.

Metric Design Rules

  1. Cover chain, not everything:

    • success/failure + duration
    • retry/terminal path (when strategy exists)
    • read/commit/write failures
    • up/down
  2. Always include count and latency for processing path.

  3. Label policy:

    • required: stable low-cardinality dimensions only
    • optional only when bounded enum (for example result, strategy, protocol)
    • service/entity name labels are optional and must pass cardinality review
    • forbidden high-cardinality labels: topic, error_message, payload-derived labels
  4. Naming:

    • use module prefix (for example mqtt_, raft_, handler_)
    • counters end with _total
    • duration histogram uses _ms
    • liveness gauge uses _up

Minimal Metric Set Template

Adapt this template to the target module:

  • <module>_messages_processed_success_total{...}
  • <module>_messages_processed_failure_total{...}
  • <module>_process_duration_ms{...} (histogram)
  • <module>_retry_total{...,strategy} (if retry exists)
  • <module>_terminal_total{...,result} (discard/dlq/drop etc., if applicable)
  • <module>_critical_step_failure_total{...} (read/write/commit/etc.)
  • <module>_up{...} (gauge)

Implementation Workflow

  1. Define metrics in src/common/metrics

    • register counters/histogram/gauge
    • add record helper APIs
    • keep API signatures consistent with chosen low-cardinality labels
  2. Insert runtime instrumentation

    • add metrics at success, failure, retry, terminal, and liveness points
  3. Fix call sites impacted by signature changes

    • update all metric helper users
    • ensure compile passes across dependent crates
  4. Validation

    • run targeted cargo check for impacted crates
    • run lints for touched files

Grafana Decision Rule

After metrics are added, decide whether to update grafana/robustmq-broker.json:

  • Update dashboard if metrics are operationally critical and stable.
  • Skip dashboard only when user explicitly says not to update or metrics are temporary.

When updating dashboard:

  • add/extend compact row
  • prioritize low-cardinality dimensions and trend panels
  • avoid panel explosion; 4-6 panels for first iteration

Output Format

Return:

  1. Metrics added/changed (names + labels)
  2. Instrumentation points (files/functions)
  3. Whether Grafana was updated and why
  4. Validation commands and results

Guardrails

  • Do not add metrics without clear operational use.
  • Do not introduce high-cardinality labels.
  • Do not duplicate semantically equivalent metrics.
  • Do not break existing metric names unless migration is requested.

Signals

GitHub stars
2k
Forks
256
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
robustmq-metrics
Source
github.com/robustmq/robustmq