/add-workers

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🚨 EXECUTION NOTICE FOR CLAUDE

When you invoke this command via SlashCommand, the system returns THESE INSTRUCTIONS below.

YOU are the executor. This is NOT an autonomous subprocess.

• ✅ The phases below are YOUR execution checklist
• ✅ YOU must run each phase immediately using tools (Bash, Read, Write, Edit, TodoWrite)
• ✅ Complete ALL phases before considering this command done
• ❌ DON't wait for "the command to complete" - YOU complete it by executing the phases
• ❌ DON't treat this as status output - it IS your instruction set

Immediately after SlashCommand returns, start executing Phase 0, then Phase 1, etc.

See @CLAUDE.md section "SlashCommand Execution - YOU Are The Executor" for detailed explanation.

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Arguments: $ARGUMENTS

Goal: Configure Celery workers with optimal pool types and concurrency settings based on project requirements

Core Principles:

• Detect existing Celery configuration before modifying
• Choose pool type based on workload characteristics
• Set concurrency based on CPU cores and task types
• Provide clear worker configuration documentation

Phase 1: Discovery Goal: Understand project structure and current worker configuration

Actions:

• Parse $ARGUMENTS for pool type preference (prefork, gevent, eventlet, solo, threads)
• Detect project type and existing Celery setup
• Example: !{bash ls celery.py celeryconfig.py worker.py tasks.py 2>/dev/null || echo "No Celery files found"}
• Find existing worker configuration:
  • @celery.py
  • @celeryconfig.py
  • @worker.py
• Check for existing pool and concurrency settings

Phase 2: Analysis Goal: Determine optimal worker configuration

Actions:

• Analyze task types if tasks.py exists
• Count CPU cores for concurrency defaults
• Example: !{bash nproc || python3 -c "import os; print(os.cpu_count())"}
• Review task characteristics:
  • CPU-bound vs I/O-bound
  • Long-running vs short tasks
  • Memory requirements
• Identify appropriate pool type:
  • prefork: CPU-bound tasks (default)
  • gevent: I/O-bound, many concurrent tasks
  • eventlet: Similar to gevent, alternative greenlet implementation
  • threads: Thread-based concurrency
  • solo: Single worker, debugging

Phase 3: Implementation Goal: Configure workers with architect agent

Actions:

Task(description="Configure Celery workers", subagent_type="celery:worker-architect", prompt="You are the celery:worker-architect agent. Configure Celery workers for $ARGUMENTS.

Current project context:

• Detected pool type preference: [from $ARGUMENTS or auto-detect]
• CPU cores available: [from nproc]
• Existing configuration: [from celeryconfig.py or celery.py]

Requirements:

• Set appropriate pool type (prefork/gevent/eventlet/threads/solo)
• Configure concurrency based on pool type and CPU cores
• Set worker autoscaling if applicable
• Configure worker prefetch settings
• Set time limits (soft/hard)
• Configure memory management (max-tasks-per-child)
• Add worker monitoring hooks if needed
• Create worker startup script

Deliverable:

• Updated worker configuration files
• Worker startup script with proper arguments
• Documentation of worker settings and rationale")

Phase 4: Verification Goal: Validate worker configuration

Actions:

• Check generated configuration files exist
• Verify pool type is set correctly
• Confirm concurrency settings are appropriate
• Review worker startup script
• Example: !{bash cat worker.py 2>/dev/null | grep -E "pool|concurrency|autoscale"}
• Test worker configuration syntax (dry run if possible)

Phase 5: Summary Goal: Document worker configuration

Actions:

• Display configured settings:
  • Pool type and rationale
  • Concurrency settings
  • Autoscaling configuration
  • Memory limits
  • Time limits
• Show worker startup command
• Provide next steps:
  • Test worker: celery -A app worker --loglevel=info
  • Monitor workers: celery -A app inspect stats
  • Scale workers: celery -A app control pool_grow N
• Document performance tuning considerations