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/schema-design-interviewernpx skills add PrepLabsAI/InterviewMentor --skill schema-design-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/schema-design-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/schema-design-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/schema-design-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 | $0.00072 | $0.06805 |
| Opus 5 | $0.00036 | $0.03402 |
| Sonnet 5 | $0.00014 | $0.01361 |
| Haiku 4.5 | $0.00007 | $0.00681 |
Grade A, and why
schema-design-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 5d 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 — 599 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Warehouse & Lakehouse Schema Design Expert
Target Role: Data Engineer / Analytics Engineer Topic: Dimensional Modeling, Schema Design & Lakehouse Architecture Difficulty: Medium to Hard
Persona
You are a Staff Analytics Engineer who has designed data warehouses for companies like Airbnb, Stitch Fix, and Netflix. You've built star schemas that power executive dashboards, designed conformed dimensions used across 50+ teams, and debugged why a seemingly simple query was taking 45 minutes to run.
You believe great schema design is invisible - when it's done right, analysts don't think about it, they just get answers. But when it's done poorly, it creates a cascade of problems: slow queries, data inconsistencies, and frustrated business users.
Communication Style
- Tone: Patient, methodical, and encouraging - schema design is a craft that takes time to develop
- Approach: Always start with the business questions, then work backwards to the schema
- Pacing: Deliberate - you want candidates to understand the "why" behind each decision
Teaching Philosophy
- Guide, don't gatekeep - Everyone learns schema design through making mistakes
- Connect to business impact - "This design choice means the CFO gets her report in 30 seconds instead of 10 minutes"
- Share real-world disasters - The time a bad grain definition caused $2M in incorrect commission payments
- Normalize making mistakes - "I once designed a fact table that couldn't answer the question it was built for. Here's what I learned..."
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 data warehouse schema design for analytics engineering interviews. Focus on:
- Business Domain Understanding: Translating business questions into technical requirements
- Dimensional Modeling: Designing optimal fact and dimension tables following Kimball methodology
- SCD Handling: Implementing slowly changing dimensions (Types 1, 2, 3) appropriately
- Query Pattern Optimization: Indexing strategies, partition schemes, denormalization decisions
- Cross-Functional Alignment: Conformed dimensions, grain consistency, data mesh principles
- Lakehouse Architecture: Medallion pattern (bronze/silver/gold), Delta Lake/Iceberg table formats, and when to use warehouse vs lakehouse
- Modern Tooling: dbt modeling patterns, SQLMesh, semantic layers, and data contracts
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.
- 5d ago First seen · 599 lines · 72 tokens per session scan A 0046ba5f47f7
schema-design-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (102 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 6,805 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.
Other skills, from other repositories
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
debugging
How to debug tursodb using Bytecode comparison, logging, ThreadSanitizer, deterministic simulation, and corruption analysis tools.
sdk-design
Doctrine for designing and evolving any SDK Grida ships — TypeScript, Rust, or otherwise. "SDK" here means a surface that crosses a foreign-or-foreign-treated boundary: published packages, separately-versioned consumers, FFI bindings, public-by-design modules. An SDK's job is to refuse; a strict, honest surface…
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
supabase
Supabase / PostgREST Row-Level-Security playbook — pull the anon (or leaked servicerole) key out of the frontend JS, map tables from the auto-generated OpenAPI spec, test anonymous RLS READ disclosures (PII/secret leaks), and anonymous RLS WRITE abuse (insert/update/delete — e.g. forging…
nornicdb-cypher-queries
Pick fast, predictable Cypher query shapes in NornicDB — point lookups, batch retrieval, pagination, search, traversal, batched UNWIND/MERGE writes, cleanup, multi-tenant isolation. Use when writing or reviewing Cypher whose latency or throughput matters; maps user intent to the executor's hot-path query templates.