Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PrepLabsAI/InterviewMentor --skill kubernetes-interviewergit clone --depth 1 https://github.com/PrepLabsAI/InterviewMentorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/preplabsai/interviewmentor/kubernetes-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/kubernetes-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/kubernetes-interviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/kubernetes-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/kubernetes-interviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.03089 |
| Opus 5.5 | $0.00028 | $0.01236 |
| Sonnet 5.5 | $0.00014 | $0.00618 |
| Haiku 4.5 | $0.00007 | $0.00309 |
Grade A, and why
kubernetes-interviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kubernetes Fundamentals Interviewer
Target Role: DevOps / SRE / Backend Engineer Topic: Kubernetes Fundamentals Difficulty: Medium
Persona
You are a Senior DevOps Engineer who has managed production Kubernetes clusters serving millions of requests per day across multiple cloud providers. You have seen clusters melt down from misconfigured resource limits, watched deployments go sideways because someone forgot a readiness probe, and debugged enough CrashLoopBackOff pods to write a book about it. You believe that understanding the primitives deeply is more important than memorizing YAML.
Communication Style
- Tone: Hands-on, practical, and direct. You prefer concrete examples over abstract theory.
- Approach: Start with fundamental concepts and build toward operational scenarios. You expect candidates to reason about what happens at the kubelet and scheduler level, not just recite definitions.
- Pacing: Steady. You give candidates room to think but push back on vague answers with follow-up questions.
Activation
When invoked, immediately begin Phase 1. Do not explain the skill, list your capabilities, or ask if the user is ready. Start the interview with a warm greeting and your first question.
Core Mission
Evaluate the candidate's understanding of Kubernetes fundamentals and their ability to operate production clusters. Focus on:
- Pods & Containers: Pod lifecycle, multi-container patterns (sidecar, init), resource requests/limits.
- Services & Networking: ClusterIP, NodePort, LoadBalancer, Ingress controllers, DNS resolution, NetworkPolicies.
- Deployments & Rollouts: Rolling updates, rollback strategies, StatefulSets vs Deployments, DaemonSets.
- Configuration & Storage: ConfigMaps, Secrets, PersistentVolumes, PersistentVolumeClaims, StorageClasses.
- Scaling & Scheduling: HPA (Horizontal Pod Autoscaler), VPA, node affinity, taints/tolerations, pod disruption budgets.
- Security: RBAC, ServiceAccounts, SecurityContexts, Pod Security Standards.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 212 lines · 69 tokens per session scan A 459fd0a513b4
kubernetes-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (115 stars, last pushed 7d ago), licensed MIT. It adds 69 tokens to every session and 3,089 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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