Cpu Performance logo

Cpu Performance

Organization
RedHatProductSecurity
cpu-performance

Configure CPU isolation, scheduling policies, storage provisioning, and image pull policies for latency-sensitive Kubernetes workloads. Use when writing, reviewing, or auditing pod specs for telco, far-edge, or real-time workloads that require deterministic CPU performance.

Overview

PublisherRedHatProductSecurity
Repositoryprodsec-skills
Skill namecpu-performance
Stars
56
Forks
9
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by RedHatProductSecurity on GitHub. Read the source before you install it.

Installation

Install the Cpu Performance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/cpu-performance
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-skills.git /tmp/prodsec-skills
mkdir -p .claude/skills
cp -r /tmp/prodsec-skills/module/skills/cpu-performance .claude/skills/cpu-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cpu Performance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cpu Performance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cpu Performance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CPU and Performance Configuration

Configure workloads for deterministic CPU performance, correct scheduling policies, and reliable storage and image management.

CPU Isolation

For workloads requiring exclusive CPU access (Guaranteed QoS with dedicated CPUs), all containers in the pod must meet these requirements:

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

  • CPU requests and limits must be identical and specified in whole units (e.g., 2, not 2.5 or 2000m)
  • runtimeClassName must be specified
  • Annotations must disable CPU and IRQ load-balancing
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: app
  annotations:
    cpu-load-balancing.crio.io: "disable"
    irq-load-balancing.crio.io: "disable"
spec:
  template:
    spec:
      runtimeClassName: performance-rt
      containers:
        - name: app
          resources:
            requests:
              cpu: "2"
              memory: 256Mi
            limits:
              cpu: "2"
              memory: 256Mi

CPU Pool Scheduling Policies

Containers must use the correct scheduling policy for their CPU pool type.

Exclusive CPU Pool

Workloads in the exclusive CPU pool must use real-time (RT) scheduling policy with priority less than 10.

Required for: Far-Edge (mandatory), all others (optional)

Isolated CPU Pool

Workloads in the isolated CPU pool must also use RT scheduling policy.

Required for: Far-Edge (mandatory), all others (optional)

Shared CPU Pool

Workloads in the shared CPU pool must use non-RT scheduling policies (e.g., SCHED_OTHER) to share CPU time fairly with other applications and kernel threads.

Required for: Far-Edge (mandatory), all others (optional)

CPU Pool Consistency

If one container in a pod selects an exclusive CPU pool, all containers in that pod must select the same CPU pool type.

Required for: Far-Edge (mandatory), all others (optional)

Image Pull Policy

Containers must use IfNotPresent as the image pull policy. This ensures pods can restart even if the image registry is temporarily unavailable.

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

yaml
containers:
  - name: app
    image: registry.example.com/app:v1.2.3
    imagePullPolicy: IfNotPresent

Pod Ownership

Workload pods must be deployed as part of a ReplicaSet (via Deployment) or StatefulSet. Do not use naked pods or DaemonSets — they lack proper lifecycle management for updates, scaling, and recovery.

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

Storage Provisioning

Persistent Volume Reclaim Policy

Persistent volumes used by workloads must have a reclaim policy of Delete. This ensures storage is cleaned up when the workload is removed from the cluster.

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

yaml
apiVersion: v1
kind: PersistentVolume
metadata:
  name: app-pv
spec:
  persistentVolumeReclaimPolicy: Delete

Storage Provisioner

Multi-node clusters must not use local storage provisioners (kubernetes.io/no-provisioner, topolvm.io). Local storage is only appropriate for single-node clusters, where only one local provisioner type should be installed (LVMS or no-provisioner).

Required for: all profiles (mandatory)

Implementation Checklist

  • CPU-isolated workloads have identical CPU requests and limits in whole units
  • CPU-isolated pods specify runtimeClassName and disable CPU/IRQ load-balancing
  • Exclusive/isolated CPU pool workloads use RT scheduling policy
  • Shared CPU pool workloads use non-RT scheduling policy (SCHED_OTHER)
  • All containers in a pod use the same CPU pool type
  • imagePullPolicy: IfNotPresent is set on all containers
  • Workloads are deployed via Deployments or StatefulSets (no naked pods)
  • Persistent volumes have persistentVolumeReclaimPolicy: Delete
  • Multi-node clusters do not use local storage provisioners

Certsuite Test Mapping

GuidanceCertsuite Test IDProfiles
CPU isolationlifecycle-cpu-isolationTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
Exclusive CPU pool RT schedulingperformance-exclusive-cpu-pool-rt-scheduling-policyFar-Edge: mandatory, all others: optional
Isolated CPU pool RT schedulingperformance-isolated-cpu-pool-rt-scheduling-policyFar-Edge: mandatory, all others: optional
Shared CPU pool non-RT schedulingperformance-shared-cpu-pool-non-rt-scheduling-policyFar-Edge: mandatory, all others: optional
CPU pool consistencyperformance-exclusive-cpu-poolFar-Edge: mandatory, all others: optional
Image pull policylifecycle-image-pull-policyTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
Pod ownershiplifecycle-pod-owner-typeTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
PV reclaim policylifecycle-persistent-volume-reclaim-policyTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
Storage provisionerlifecycle-storage-provisionerAll profiles: mandatory

References

Frequently asked questions

What does the Cpu Performance AI skill do?

Configure CPU isolation, scheduling policies, storage provisioning, and image pull policies for latency-sensitive Kubernetes workloads. Use when writing, reviewing, or auditing pod specs for telco, far-edge, or real-time workloads that require deterministic CPU performance.

Why use Cpu Performance on TypingMind?

Because you install it once and use it with any model. Cpu Performance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cpu Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/cpu-performance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cpu Performance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cpu Performance?

As many as you like. As long as a model supports skills, you can use Cpu Performance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cpu Performance AI skill free?

Yes. It is published on GitHub by RedHatProductSecurity under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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