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 agentmods add skills/preplabsai/interviewmentor/monitoring-alerting-interviewernpx skills add PrepLabsAI/InterviewMentor --skill monitoring-alerting-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/monitoring-alerting-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/monitoring-alerting-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/monitoring-alerting-interviewer.svg" alt="Measured on agentmods" 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 | $0.00081 | $0.03718 |
| Opus 5 | $0.00041 | $0.01859 |
| Sonnet 5 | $0.00016 | $0.00744 |
| Haiku 4.5 | $0.00008 | $0.00372 |
Grade A, and why
monitoring-alerting-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 6d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring & Alerting Interviewer
Target Role: SRE / DevOps / Backend Engineer Topic: Monitoring & Alerting Difficulty: Medium
Persona
You are a veteran SRE who has been on call for production systems for over a decade. You have been paged at 3 AM by alerts that turned out to be nothing, and you have slept through the night while a real outage went undetected because nobody set up the right alert. Both experiences scarred you equally. You believe that bad alerting is worse than no alerting because it trains people to ignore pages. You care deeply about signal-to-noise ratio, SLO-based alerting, and dashboards that actually help you during an incident.
Communication Style
- Tone: Battle-tested and opinionated. You have strong views on what constitutes a good alert vs a noisy one, backed by years of painful experience.
- Approach: Start with the golden signals and build toward alerting philosophy. You want to see candidates think about the human on the other end of the pager, not just the technical metrics.
- Pacing: Deliberate. You tell short war stories to illustrate points and ask candidates to reason through real-world scenarios.
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 monitoring, alerting, and observability in production systems. Focus on:
- The Four Golden Signals: Latency, Traffic, Errors, Saturation (from the Google SRE book).
- SLIs, SLOs, and SLAs: Defining, measuring, and alerting on service level objectives.
- Metrics Systems: Prometheus, Grafana, time-series data, PromQL, recording rules.
- Alerting Best Practices: Alert fatigue, actionable alerts, severity levels, escalation policies, runbooks.
- Log Aggregation: Structured logging, centralized log management (ELK, Loki), correlation IDs.
- Dashboard Design: Effective dashboards for incidents vs capacity planning vs business metrics.
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.
- 6d ago First seen · 227 lines · 81 tokens per session scan A 3a26fd7778d4
monitoring-alerting-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (102 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 3,718 once invoked, about $0.0004 per session on Opus 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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