Inspect Redis broker data to diagnose Celery task queue health.
npx skills add https://github.com/michaelayoade/dotmac_crm --skill celery-monitor
Please help me install this Agent Skill. Skill: celery-monitor Source: https://github.com/michaelayoade/dotmac_crm/tree/main/.claude/skills/celery-monitor Command: npx skills add https://github.com/michaelayoade/dotmac_crm --skill celery-monitor
Celery systems often fail silently or slow down due to backlogs, dead workers, or stuck/failed tasks, and it can be difficult to pinpoint the root cause quickly.
Use the celery-monitor skill to inspect queue depth, worker heartbeats, and failed or stuck tasks in Redis MCP for your Celery deployment.
High-intent search queries and answers about installing and using this skill.
Diagnosing a Celery queue backlog involves inspecting the Redis list backing the default queue to detect pending tasks. Analyzing these Redis data structures reveals queue depth and helps pinpoint the root cause of task processing delays.
Checking Celery worker health with Redis involves querying for heartbeat key patterns to confirm liveness. Locating these heartbeat keys verifies that workers are actively running and processing tasks within the deployment.
Identifying failed or stuck Celery tasks requires inspecting task-meta JSON state fields in the Redis result backend. Examining status, date_done, and traceback fields surfaces failed tasks and detects long-running STARTED tasks that may be stuck.
Yes, this approach to Celery monitoring requires querying Redis directly. It inspects Redis broker and result-backend data structures, interpreting list, set, and string patterns to evaluate queue health, worker liveness, and task states.
Celery task results accumulate in Redis when task-meta entries are not expiring properly. Counting task-meta keys and understanding expiry behavior estimates result backend hygiene and determines whether results are accumulating unnecessarily.
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