Observability logo

Observability

Organization
RedHatProductSecurity
observability

Configure Kubernetes workload observability including container logging, CRD status reporting, port naming conventions, image tagging, and API compatibility. Use when writing, reviewing, or auditing CRDs, container specs, Services, or preparing for OpenShift version upgrades.

Overview

PublisherRedHatProductSecurity
Repositoryprodsec-skills
Skill nameobservability
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 Observability 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/observability
  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/observability .claude/skills/observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Observability 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 Observability 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 Observability 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.

Workload Observability

Configure workloads for operational visibility by following logging, naming, tagging, and API compatibility best practices.

Container Logging

Containers must log to stdout and stderr. Do not redirect logs to files inside the container — platform log collection (Fluentd, Vector, Loki) relies on capturing stdout/stderr streams.

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

yaml
containers:
  - name: app
    image: registry.example.com/app:v1.2.3
    command: ["/app"]

Avoid patterns like redirecting output to a log file:

/app > /var/log/app.log 2>&1  # Don't do this

CRD Status Sub-Resource

All Custom Resource Definitions must include a status sub-resource specification. The status sub-resource enables the platform to track the state of custom resources and allows controllers to report status separately from spec updates.

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

yaml
apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.app.example.com
spec:
  group: app.example.com
  names:
    kind: MyResource
    plural: myresources
  scope: Namespaced
  versions:
    - name: v1
      served: true
      storage: true
      subresources:
        status: {}
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
            status:
              type: object

Port Naming Conventions

Container port names must follow the format <protocol>[-<suffix>]. This convention enables service mesh integration and platform-level traffic management.

Required for: Extended (mandatory), all others (optional)

ValidInvalid
httpmy-port
http-apiport-8080
grpc-dataapp
tcp-metricsmetrics
yaml
containers:
  - name: app
    ports:
      - containerPort: 8080
        name: http-api
      - containerPort: 9090
        name: http-metrics
      - containerPort: 50051
        name: grpc-data

Image Tagging

All container images must have explicit tags. Do not rely on the implicit latest tag — it makes deployments non-reproducible and prevents rollback.

Required for: Extended (mandatory), all others (optional)

yaml
containers:
  - name: app
    image: registry.example.com/app:v1.2.3   # Good: explicit tag
    # image: registry.example.com/app         # Bad: implicit latest
    # image: registry.example.com/app:latest  # Bad: explicit latest

API Compatibility

Workload APIs must be compatible with the next OpenShift version. Before upgrading, verify that no deprecated or removed APIs are in use by the workload's CRDs, webhooks, or API calls.

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

Use oc api-resources and the Kubernetes API deprecation guide to check for deprecated APIs before platform upgrades.

Implementation Checklist

  • All containers log to stdout/stderr (no log file redirection)
  • All CRDs have status sub-resource defined
  • Container port names follow <protocol>[-<suffix>] format
  • All container images have explicit tags (not latest)
  • Workload APIs are compatible with the next OpenShift version

Certsuite Test Mapping

GuidanceCertsuite Test IDProfiles
Log to stdout/stderrobservability-container-loggingTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
CRD status sub-resourceobservability-crd-statusTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
Port naming conventionmanageability-container-port-name-formatExtended: mandatory, all others: optional
Explicit image tagsmanageability-containers-image-tagExtended: mandatory, all others: optional
API compatibilityobservability-compatibility-with-next-ocp-releaseTelco/Far-Edge/Extended: mandatory, Non-Telco: optional

References

Frequently asked questions

What does the Observability AI skill do?

Configure Kubernetes workload observability including container logging, CRD status reporting, port naming conventions, image tagging, and API compatibility. Use when writing, reviewing, or auditing CRDs, container specs, Services, or preparing for OpenShift version upgrades.

Why use Observability on TypingMind?

Because you install it once and use it with any model. Observability 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 Observability in TypingMind?

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

Which AI models can use Observability?

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 Observability?

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

Is the Observability 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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