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autoscale-audit

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Atualizado2 de julho de 2026 às 01:54

Inventory and report Kubernetes autoscaling stored in ConfigHub across a fleet with the cub-autoscale CLI — HorizontalPodAutoscalers and KEDA ScaledObjects: each one's scale target, min/max replicas, and whether it is pinned (min == max). Use for "what autoscalers do we have?", "list our HPAs / ScaledObjects", "which workloads are autoscaled?", "per-cluster autoscaler counts", "is autoscaler X pinned?". Not for autoscaling findings/anti-patterns (use autoscale-findings), edits or HPA→KEDA conversion (use autoscale-edit), or live cluster state (use kubectl); analysis is over ConfigHub-managed Units only.

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confighub

confighub/examples

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Administradores de redes e sistemas de computadorInformática e Matemática·SOC 15-1244

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SKILL.md

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name	autoscale-audit
description	Inventory and report Kubernetes autoscaling stored in ConfigHub across a fleet with the cub-autoscale CLI — HorizontalPodAutoscalers and KEDA ScaledObjects: each one's scale target, min/max replicas, and whether it is pinned (min == max). Use for "what autoscalers do we have?", "list our HPAs / ScaledObjects", "which workloads are autoscaled?", "per-cluster autoscaler counts", "is autoscaler X pinned?". Not for autoscaling findings/anti-patterns (use autoscale-findings), edits or HPA→KEDA conversion (use autoscale-edit), or live cluster state (use kubectl); analysis is over ConfigHub-managed Units only.
phase	verify
allowed-tools	Bash(cub-autoscale --help) Bash(cub-autoscale * --help) Bash(cub auth status) Bash(cub-autoscale preflight) Bash(cub-autoscale snapshot) Bash(cub-autoscale snapshot ) Bash(cub-autoscale list) Bash(cub-autoscale list )

autoscale-audit

Report the autoscaling ConfigHub holds — HorizontalPodAutoscalers and KEDA ScaledObjects — across the fleet. Read-only.

Why this matters

cub-autoscale loads a fleet snapshot of autoscalers and the workloads they target, so you can see at a glance which workloads scale, what their replica bounds are, and which autoscalers are pinned (min == max, so they can't actually scale). Clusters are ConfigHub Targets (the Space slug stands in for unbound Units). Analysis is over ConfigHub-managed Units only. Output is JSON by default; add -o table for humans.

When to use

"What autoscalers do we have?" / "audit autoscaling." → list.
"List our HPAs / ScaledObjects." → list (KIND column).
"Which workloads are autoscaled / not?" → snapshot (AUTOSCALED vs WORKLOADS).
"Per-cluster autoscaler counts." → snapshot.
"Is autoscaler X pinned?" → list (PINNED column).

Do not load for

Autoscaling anti-patterns, ranked (pinned, no-autoscaler, PDB-vs-minReplicas) — use autoscale-findings.
Editing an HPA, converting HPA→KEDA, applying a profile — use autoscale-edit.
Live cluster state (current replica count, HPA status) — kubectl.

Preflight gates

cub-autoscale preflight succeeds. If it fails, ask the user to run cub auth login (interactive) and retry.

The toolkit

cub-autoscale snapshot -o table — per-cluster HPA / ScaledObject / workload / autoscaled / PDB counts.
cub-autoscale list -o table — per-autoscaler kind, scale target, min/max, and whether it's pinned.

Scoping

Server-side --where plus label shorthands (--component, --environment, --region, --owner, --layer, --variant), AND-only.

Stop conditions

Snapshot empty — report it; suggest widening scope or checking cub auth status.
The question is about anti-patterns or fixing autoscaling — hand off to autoscale-findings / autoscale-edit.

Tool boundary

Read-only. Never use kubectl to answer a ConfigHub-state question.

References

cub-autoscale snapshot --help, cub-autoscale list --help.
Companion skills: autoscale-findings, autoscale-edit.

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autoscale-edit

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Set Kubernetes autoscaling as config-as-data with the cub-autoscale CLI — edit a HorizontalPodAutoscaler's min/max replicas and cpu/memory targets, convert an HPA to a KEDA ScaledObject, apply reusable autoscaling profiles to one Unit or across a whole selector, and promote changes downstream. Use for "raise the HPA max to 20", "set cpu target to 60%", "convert this HPA to KEDA", "make every prod HPA scale out earlier", "apply the hpa-range profile", "roll the autoscaling change downstream". Dry-run by default; requires --commit --change-desc. Not for read-only checks (use autoscale-audit / autoscale-findings) or enforcement Triggers (use autoscale-guardrails).

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autoscale-findings

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Produce severity-ranked Kubernetes autoscaling findings across a ConfigHub fleet with the cub-autoscale CLI. Flags autoscalers that are pinned (min == max, can't scale), workloads with no HPA/ScaledObject, and the cross-resource case where a PodDisruptionBudget's minAvailable blocks all voluntary eviction at the autoscaler's minReplicas. Use for "what autoscaling problems do we have?", "which HPAs can't scale?", "which workloads lack an autoscaler?", "is any PDB blocking scale-down?". Not for the raw autoscaler inventory (use autoscale-audit), fixing autoscaling (use autoscale-edit), or live cluster state (use kubectl); analysis is over ConfigHub-managed Units only.

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Install and inspect the autoscaling enforcement pack with the cub-autoscale CLI — an autoscale-policy Space with two Warn=true Triggers: a vet-cel rule that flags any HPA/ScaledObject that is pinned (min == max), and a vet-schemas rule that schema-validates every mutation (the post-convert check for convert-keda output, since keda.sh is in the schema catalog) — wired to in-scope Spaces via a shared Trigger Filter. guardrails install (dry-run by default) and status (Units with ApplyWarnings/Gates). Use for "enforce that no HPA is pinned", "make sure converted ScaledObjects pass schema validation", "which autoscalers are flagged?". Not for read-only findings (use autoscale-findings) or fixing autoscaling (use autoscale-edit).

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observability-audit

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Inventory the observability posture of Kubernetes workloads stored in ConfigHub across a fleet with the cub-observability CLI — ServiceMonitors, Services (and which expose metrics), and telemetry-sidecar presence. Use for "what ServiceMonitors / Services do we have?", "which Services expose metrics?", "which workloads have an otel sidecar?", "per-cluster observability counts". Not for ServiceMonitor coverage gaps (use observability-findings), fixes (use observability-instrument / observability-guardrails), or live cluster state (use kubectl); analysis is over ConfigHub-managed Units only.

2026-07-014

name	autoscale-audit
description	Inventory and report Kubernetes autoscaling stored in ConfigHub across a fleet with the cub-autoscale CLI — HorizontalPodAutoscalers and KEDA ScaledObjects: each one's scale target, min/max replicas, and whether it is pinned (min == max). Use for "what autoscalers do we have?", "list our HPAs / ScaledObjects", "which workloads are autoscaled?", "per-cluster autoscaler counts", "is autoscaler X pinned?". Not for autoscaling findings/anti-patterns (use autoscale-findings), edits or HPA→KEDA conversion (use autoscale-edit), or live cluster state (use kubectl); analysis is over ConfigHub-managed Units only.
phase	verify
allowed-tools	Bash(cub-autoscale --help) Bash(cub-autoscale * --help) Bash(cub auth status) Bash(cub-autoscale preflight) Bash(cub-autoscale snapshot) Bash(cub-autoscale snapshot ) Bash(cub-autoscale list) Bash(cub-autoscale list )

autoscale-audit

Report the autoscaling ConfigHub holds — HorizontalPodAutoscalers and KEDA ScaledObjects — across the fleet. Read-only.

Why this matters

When to use

"What autoscalers do we have?" / "audit autoscaling." → list.
"List our HPAs / ScaledObjects." → list (KIND column).
"Which workloads are autoscaled / not?" → snapshot (AUTOSCALED vs WORKLOADS).
"Per-cluster autoscaler counts." → snapshot.
"Is autoscaler X pinned?" → list (PINNED column).

Do not load for

Autoscaling anti-patterns, ranked (pinned, no-autoscaler, PDB-vs-minReplicas) — use autoscale-findings.
Editing an HPA, converting HPA→KEDA, applying a profile — use autoscale-edit.
Live cluster state (current replica count, HPA status) — kubectl.

Preflight gates

cub-autoscale preflight succeeds. If it fails, ask the user to run cub auth login (interactive) and retry.

The toolkit

cub-autoscale snapshot -o table — per-cluster HPA / ScaledObject / workload / autoscaled / PDB counts.
cub-autoscale list -o table — per-autoscaler kind, scale target, min/max, and whether it's pinned.

Scoping

Server-side --where plus label shorthands (--component, --environment, --region, --owner, --layer, --variant), AND-only.

Stop conditions

Snapshot empty — report it; suggest widening scope or checking cub auth status.
The question is about anti-patterns or fixing autoscaling — hand off to autoscale-findings / autoscale-edit.

Tool boundary

Read-only. Never use kubectl to answer a ConfigHub-state question.

References

cub-autoscale snapshot --help, cub-autoscale list --help.
Companion skills: autoscale-findings, autoscale-edit.