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 sethdford/claude-skills --skill interview-prepgit clone --depth 1 https://github.com/sethdford/claude-skillsWrote 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/sethdford/claude-skills/interview-prep)<a href="https://agentmods.dev/skills/sethdford/claude-skills/interview-prep"><img src="https://agentmods.dev/badge/skills/sethdford/claude-skills/interview-prep.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 | $0.00031 | $0.00671 |
| Opus 5 | $0.00015 | $0.00336 |
| Sonnet 5 | $0.00006 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
interview-prep 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 8d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Prep
Prepare for architecture interview questions and system design scenarios.
Context
You are preparing for architecture interviews. Practice system design problems, articulate trade-offs, communicate clearly under pressure. Study common patterns and real-world constraints.
Domain Context
Based on architecture interview best practices:
- System Design Questions: "Design Instagram", "Design distributed cache", "Design payment system"
- Communication Skills: Clarify requirements, think out loud, accept feedback, adjust design
- Trade-off Discussion: Consistency vs availability, latency vs cost, flexibility vs simplicity
- Real-World Constraints: Assume realistic team size, budget, deployment platform
Instructions
-
Study Common Systems:
- Social media (Facebook, Instagram): scale, feed generation, notifications
- Payments (Stripe, PayPal): transactions, fraud detection, reconciliation
- Search (Google): indexing, ranking, distributed retrieval
- Messaging (WhatsApp, Slack): realtime, reliability, scalability
-
Practice System Design Problem:
- Clarify: Scale (1M users, 1B records)? Growth (100x in 2 years)? Consistency (strong or eventual)?
- Sketch: Components, data model, communication, scaling strategy
- Discuss: What would you change at 10x scale? Where are bottlenecks? What are risks?
-
Prepare Answers to Common Questions:
- "How would you scale this to 10x?"
- "How does this fail?"
- "What's the biggest risk?"
- "How would you monitor this?"
- "What would you do differently with more resources?"
-
Practice Communication:
- Think out loud. "I'm thinking about SQL vs NoSQL because..."
- Accept feedback. "Good point, that would break my design. Let me reconsider..."
- Ask clarifying questions. "How much data? How many users? What's the latency requirement?"
-
Mock Interviews: Practice with peer or mentor. Get feedback on communication, technical depth, trade-off reasoning.
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.
- 8d ago First seen · 62 lines · 31 tokens per session scan A 5ba065582dc7
interview-prep is a skill published in the GitHub repository sethdford/claude-skills (39 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 671 once invoked, about $0.0002 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
cmd_docs
Legacy slash-entry shim for the documentation-lookup skill. Prefer the skill directly.
code-tour
Create CodeTour .tour files — persona-targeted, step-by-step walkthroughs with real file and line anchors. Use for onboarding tours, architecture walkthroughs, PR tours, RCA tours, and structured "explain how this works" requests.
documentation-lookup
Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).
explain-this
Explain whatever the user is pointing at right now in plain language: a pending question, a piece of code, an error, a command output, or an artifact like a plan or findings report. Use when the user asks to "explain this", "what am I being asked", "what's happening right now", "help me understand this", "what does…
understand-change
Teach the user to deeply understand a change through interactive tutoring: restating understanding, drilling into why/what/how, and quizzing until mastery. The active counterpart to a one-shot explanation. Use when the user asks to "understand this change", "teach me this change", "help me understand what changed"…
lookup
Look up help topics with progressive depth. Pair with attune-ai for the full author-and-read workflow. Triggers on: lookup, look up, depth, topic, help topic, tell me more.