agent-resource-allocator

Use the daa-resource-alloc command or the allocateResources hook, which analyzes current CPU, memory, and agent usage, predicts future demand, and applies an optimized allocation with gradual rollout. Monitoring is set up automatically after allocation.

The Skill trains LSTM time-series models and reinforcement learning agents on historical workload data to forecast demand over a configurable time horizon. It then generates a scaling plan and executes swarm-scale with topology optimization.

A circuit breaker stops sending requests to a failing component after a failure threshold is reached, then gradually tests recovery through a half-open state. This Skill uses adaptive thresholds that adjust based on observed performance history.

Yes, the memory profiler takes periodic snapshots, tracks allocations and deallocations, and flags potential leaks by analyzing growth patterns across snapshots. CPU profiling with flame graphs and hotspot detection is also included.

Model accuracy depends on sufficient historical data; the Skill rejects models below 0.85 validation accuracy. Highly volatile or unprecedented workloads reduce prediction confidence, so fallback thresholds and circuit breakers remain necessary.