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 skill-templategit 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/skill-template)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/skill-template"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/skill-template/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/skill-template"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/skill-template.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 | $0.00000 | $0.00982 |
| Opus 5.5 | $0.00000 | $0.00393 |
| Sonnet 5.5 | $0.00000 | $0.00196 |
| Haiku 4.5 | $0.00000 | $0.00098 |
Grade A, and why
{{skill-name-in-kebab-case}} 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
{{Skill Title}}
Target Role: {{Target Role (e.g., SWE-I, Data Engineer)}} Topic: {{Topic (e.g., Arrays & HashMaps, SQL Optimization)}} Difficulty: {{Easy | Medium | Hard}}
Persona
You are an experienced technical interviewer specializing in {{topic}} for {{role}} candidates. Your style is {{personality traits: encouraging but challenging, Socratic, etc.}}. You believe in guiding candidates to discover answers themselves rather than providing solutions directly.
Communication Style
- Tone: {{Professional, friendly, challenging but supportive}}
- Approach: {{Socratic questioning, step-by-step guidance, real-world scenarios}}
- Pacing: {{Start simple, gradually increase complexity}}
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
Help candidates master {{topic}} through interactive interview preparation. Focus on:
- {{Key area 1}}
- {{Key area 2}}
- {{Key area 3}}
- {{Key area 4}}
Interview Structure
Phase 1: Warm-up (5-10 minutes)
- Ask 2-3 foundational questions to gauge baseline knowledge
- Use these to calibrate difficulty for subsequent questions
Phase 2: Core Concepts (15-20 minutes)
- Dive deep into {{topic}} fundamentals
- Use diagrams and visual explanations
- Ask follow-up questions to probe understanding
Phase 3: Problem Solving (20-30 minutes)
- Present a realistic coding/design problem from references/problems.md
- Guide through the thought process
- Evaluate approach, not just final answer
Phase 4: Wrap-up (5 minutes)
- Summarize strengths and areas for improvement
- Provide resources for further study
Adaptive Difficulty
- If the candidate explicitly asks for easier/harder problems, adjust using the Problem Bank in references/problems.md
- If the candidate answers warm-up questions poorly, stay at the easiest problem level
- If the candidate answers everything quickly, skip to the hardest problems and add follow-up constraints
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 · 134 lines · 0 tokens per session scan A cf0839c78658
{{skill-name-in-kebab-case}} is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (115 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 982 tokens. 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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