Agent skill · software engineering · jeremylongshore
castai-performance-tuning
Tune CAST AI node and workload autoscaling against application SLOs, scheduling constraints, and recommendation confidence. Use when optimization causes latency, disruption, slow provisioning, or unstable replica and resource behavior. Trigger with: "tune CAST AI performance", "stabilize CAST AI autoscaling", "fix CAST AI scaling latency".
Why this skill is useful
Adds executable scripts for optimizing CAST AI autoscaler performance and node provisioning that are not widely documented.
What it needs
Requires curl, helm installed locally. Requires castai account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
CAST AI Performance Guardrail Tuning Overview Tune from evidence across workload demand, requests, replicas, scheduling, and nodes. Keep vertical, horizontal, and node changes separate so a lower bill never hides degraded service. Prerequisites Workload SLOs, error budget, traffic profile, and representative observation window Effective scaling policy, annotations, HPA, PDB, node templates, and cluster limits Metrics-server and healthy CAST AI components Instructions Step 1: Build the timeline Use Read and Grep to align request rate, latency, errors, pod requests, replicas, pending time, evictions, node provisioning, and policy changes. Identify whether the symptom precedes or follows CAST AI action. Step 2: Inspect effective workload policy Check policy assignment, recommendation percentile, overhead, optimization threshold, minimum and maximum resources, confidence, and automation state. Invalid annotation YAML is ignored; an invalid policy name can fall back to a system policy, so verify effective state rather than intended text. Step 3: Choose application mode Use Immediate mode only when evictions are acceptable and PDB behavior is proven. Use Deferred mode when recommendations should apply at natural recreation. Account for recommendation confidence and gradual behavior on newly onboarded clusters. Step 4: Reconcile horizontal scaling Inspect the native autoscaling/v2 HPA, metric targets, replica bounds, stabilization, and ownership. Avoid competing HPA controllers. When CAST AI takes ownership, treat that as a configuration migration with explicit rollback. Step 5: Reconcile node capacity Use Bash(kubectl:\) to examine pending reasons, affinities, topology, taints, resource shape, and scheduling events. Review node templates and maximum CPU limits. More permissive capacity is not automatically safer or cheaper. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/castai-performance-tuning