Run continuous experiment loops to optimize Kubernetes and OpenShift workloads.
npx skills add https://github.com/kcns008/clusterclaw --skill autosearch
Please help me install this Agent Skill. Skill: autosearch Source: https://github.com/kcns008/clusterclaw/tree/main/.claude/skills/autosearch Command: npx skills add https://github.com/kcns008/clusterclaw --skill autosearch
Autosearch eliminates guesswork in Kubernetes and OpenShift performance tuning by running measured experiment loops, comparing results against a baseline, and keeping only changes that produce real improvement.
Use the autosearch skill to optimize a Kubernetes service by defining a metric, establishing a baseline, and iterating on one change at a time.
High-intent search queries and answers about installing and using this skill.
Continuously optimize Kubernetes workloads by executing measured experiment loops against baseline metrics, applying one change at a time and committing only configurations that produce real performance improvements.
Automate performance tuning across OpenShift and EKS by running continuous experiment loops that measure rollout timing and throughput, comparing results against a baseline, and reverting changes that fail health checks.
Yes, you need kubectl, jq, curl, and time installed to collect metrics, execute performance experiments, and measure rollout timing and throughput gains across your Kubernetes deployments.
Yes, preserve experiment history across restarts using persistent session tracking with log files and a living progress document that records outcomes, baseline comparisons, and commit-on-success logging.
Autonomous resource tuning handles bad changes by recording experiment outcomes, reverting failed configurations, and validating results with health checks and rollout measurements before committing winning configurations.
Kubernetes performance tuning requires baseline comparisons and one change at a time to accurately measure the impact of each resource adjustment and ensure only real improvements are committed to the workload configuration.
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