AI Agent & Automation Workflows

The meta-intelligence layer that coordinates all agents, resolves conflicts, allocates resources, and ensures the multi-agent system operates as a coherent whole. Triggers on: any request involving cross-agent coordination, task routing, priority arbitration, conflict resolution, sprint lifecycle management, system health monitoring, resource allocation, workflow sequencing, or inter-agent communication routing. Also triggers AUTOMATICALLY when any agent sends an escalation message, when task dependencies create blocking conditions, when quality metrics degrade below thresholds, or when the sprint cycle transitions between phases. This agent is always active — it is the system's nervous system.

AI agent that detects and recovers from production incidents — minimizing downtime and customer impact through structured response processes. For AI products, includes detecting model degradation, inference pipeline failures, GPU resource exhaustion, and streaming endpoint outages. Use this skill to: configure PagerDuty or Opsgenie alerting, write automated runbooks for common failures, conduct blameless post-mortem analysis, track SLA compliance, design on-call rotations, implement incident severity classification, or build automated remediation workflows. Trigger on "incident", "PagerDuty", "Opsgenie", "alerting", "runbook", "post-mortem", "SLA", "on-call", "outage", "downtime", "incident response", or when production failures need structured detection and recovery processes.

AI agent that designs, trains, and iterates on the model itself — the core differentiator of any AI product. Use this skill to: design model architectures for specific tasks, fine-tune foundation models with LoRA/QLoRA/full fine-tuning, implement distributed training across multiple GPUs, build training loops with proper loss functions and optimization schedules, select and evaluate base models for transfer learning, implement data augmentation strategies, design model ensembles, optimize model size for deployment constraints, debug training failures like loss divergence or gradient explosion, or make any decision about how the model is built. Trigger on "model training", "fine-tuning", "LoRA", "QLoRA", "PyTorch", "TensorFlow", "JAX", "model architecture", "distributed training", "transfer learning", "loss function", "optimizer", "learning rate", "gradient", "PEFT", "foundation model", "base model", "model selection", or when the AI model itself needs to be designed, trained, or improved.