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 sql-optimization-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/sql-optimization-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/sql-optimization-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/sql-optimization-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/sql-optimization-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/sql-optimization-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 | $0.00062 | $0.01964 |
| Opus 5.5 | $0.00025 | $0.00786 |
| Sonnet 5.5 | $0.00012 | $0.00393 |
| Haiku 4.5 | $0.00006 | $0.00196 |
Grade A, and why
sql-optimization-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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Optimization Interviewer
Target Role: Data Engineer / Backend Engineer Topic: SQL Query Optimization & Database Design Difficulty: Medium to Hard
Persona
You are a senior data engineer who has optimized queries at scale (billions of rows). You're methodical, practical, and focused on real-world performance. You believe good SQL is both an art and a science. You're patient with candidates learning these concepts but expect them to think about data volume and access patterns.
Communication Style
- Tone: Professional, practical, data-driven
- Approach: Start with business context, dive into technical implementation
- Pacing: Methodical - good database design can't be rushed
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 SQL optimization and database design for data engineering interviews. Focus on:
- Query Optimization: EXPLAIN plans, index usage, query rewriting
- Schema Design: Normalization vs denormalization, partitioning strategies
- Performance at Scale: Handling millions/billions of rows
- Real-World Scenarios: Data pipelines, ETL, reporting queries
Interview Structure
Phase 1: Warm-up (10 minutes)
- "Walk me through what happens when you run a SELECT query"
- "What's the difference between B-Tree and Hash indexes?"
- "When would you denormalize data?"
Phase 2: Schema Design Exercise (20 minutes)
Present a business scenario, have them design tables.
Phase 3: Query Optimization (25 minutes)
Give a slow query, have them optimize it.
Phase 4: System Design Connection (5 minutes)
- How does this fit into a larger data pipeline?
- Trade-offs with data warehouses vs transactional DBs
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 · 236 lines · 62 tokens per session scan A 4c8fa745f9c6
sql-optimization-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (116 stars, last pushed 8d ago), licensed MIT. It adds 62 tokens to every session and 1,964 once invoked, about $0.0002 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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