inlined-refs — consumer lag dashboarding
SkillMonitoring & opsGenerate a Kafka consumer group lag dashboard. Use when the user asks to monitor lag, build a dashboard for consumer lag, or wire up Prometheus exporters for Kafka. Do NOT trigger for producer metrics, broker JMX, or Streams-specific monitoring.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the inlined-refs skill
What this skill tells your AI
The instructions your AI receives, as published by confluentinc/agent-skills in skills/confluent-skill-reviewer/evals/mock-skills/inlined-refs/SKILL.md and read by ahel’s review.
Step 1 — pick the exporter
We support two exporters. Read the comparison below before recommending.
Exporter comparison (from references/exporters.md)
kafka-exporter (Danielqsj)
Pros: lightweight, single binary, exposes per-group lag directly.
Cons: stateful (consumes __consumer_offsets internally), can lag on large clusters.
Best for: clusters < 100 brokers, < 1000 consumer groups.
Config snippet:
kafka-exporter \
--kafka.server=localhost:9092 \
--kafka.version=3.5.0 \
--web.listen-address=:9308
Confluent Control Center
Pros: official, covers full Confluent Platform metrics. Cons: paid licensing, heavier footprint.
Best for: enterprise Confluent Platform users.
Step 2 — Grafana dashboard
Use Grafana dashboard 7589 for kafka-exporter. Filter by consumer group, plot kafka_consumergroup_lag over a 5-minute window.
Step 3 — alerts
Set lag > 10,000 messages for 5 minutes as the default alert. Adjust per topic SLA.
Signals
- GitHub stars
- 58
- Forks
- 11
- Last commit
- Sep 2026
Advanced
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inlined-refs- Source
- github.com/confluentinc/agent-skills