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 opencue/cuecards --skill interview-prep-generatorgit clone --depth 1 https://github.com/opencue/cuecardsWrote 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/opencue/cuecards/interview-prep-generator)<a href="https://agentmods.dev/skills/opencue/cuecards/interview-prep-generator"><img src="https://agentmods.dev/badge/skills/opencue/cuecards/interview-prep-generator/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/opencue/cuecards/interview-prep-generator"><img src="https://agentmods.dev/badge/skills/opencue/cuecards/interview-prep-generator.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 | $0.00018 | $0.02734 |
| Opus 5.5 | $0.00007 | $0.01094 |
| Sonnet 5.5 | $0.00004 | $0.00547 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade A, and why
interview-prep-generator 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.
This is a copy
100% identical to interview-prep-generator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Prep Generator
When to Use This Skill
Use this skill when the user wants to:
- Prepare for a job interview
- Practice answering interview questions
- Create STAR stories from their experience
- Anticipate questions for a specific role
- Mentions: "interview prep", "prepare for interview", "STAR stories", "interview questions", "behavioral questions"
Core Capabilities
- Generate role-specific interview questions
- Create STAR stories from resume bullets
- Predict questions based on job description
- Prepare answers for common questions
- Create talking points for each experience
- Identify potential concerns and prepare responses
Interview Preparation Framework
Phase 1: Role Analysis
- Extract likely questions from job description
- Identify skills that will be tested
- Research company interview style
Phase 2: Story Banking
- Convert resume bullets into STAR stories
- Create stories for common competencies
- Practice concise delivery
Phase 3: Mock Preparation
- Practice common questions
- Prepare questions to ask
- Research company-specific topics
The STAR Method Detailed
Structure
- Situation: Set the context (1-2 sentences)
- Task: Describe your responsibility (1 sentence)
- Action: Explain what YOU did (2-3 sentences)
- Result: Share the outcome with metrics (1-2 sentences)
STAR Story Template
SITUATION: "At [Company], we faced [specific challenge/context]..."
TASK: "I was responsible for [specific ownership]..."
ACTION: "I [specific action 1], [specific action 2], and [specific action 3]..."
RESULT: "As a result, [quantified outcome]. This led to [business impact]."
Example STAR Story
Question: "Tell me about a time you led a team through a difficult project."
Answer:
SITUATION: "At TechCorp, our main product was losing customers to a competitor who had launched a better mobile experience. We were seeing 5% monthly churn, up from our normal 2%."
TASK: "As the product manager, I was responsible for turning around our mobile product to stop the bleeding and win back customers."
ACTION: "I started by interviewing 30 churned customers to understand exactly why they left. Based on that research, I prioritized 5 critical features that would achieve parity with competitors. I then worked with engineering to restructure our roadmap, negotiated with leadership to add 2 contract developers, and implemented weekly sprint reviews to keep the project on track. I also started a beta program with 50 of our best customers to get feedback before full launch."
RESULT: "We launched the improved mobile app in 3 months, reducing churn from 5% back to 2% within 60 days. We recovered 35% of churned customers and the NPS for our mobile app increased from 32 to 58. This project was recognized in our company all-hands as a turnaround success."
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 · 376 lines · 18 tokens per session scan A 1c543ab0eb22
interview-prep-generator is a skill published in the GitHub repository opencue/cuecards (6 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 2,734 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to interview-prep-generator, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
stacks
Load stack-specific knowledge (conventions, exact lint/test/build commands, common failure patterns) onto the current role agent for the project's detected tech stack(s) — Next.js, React Native, Spring Boot, FastAPI, Django, Rust, etc. Use at the start of any task in an unfamiliar or newly-onboarded repo, or whenever…
tips
Print the short addit-harness guide (five tips). Run only when the user asks for it.
mac-deep-clean
Deep-clean a macOS dev machine — reclaim disk (caches, dev-tool caches, dead repos, git worktrees, Android/Xcode/simulator bloat, System Data) and diagnose/fix memory exhaustion (runaway process leaks, swap thrashing, wired RAM). Use when disk is full, "System Data" is huge, the machine is swapping/slow, or the user…
caveman
Ultra-compressed communication mode. Cuts token usage 75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or…
learning
Pedagogical coding mode - explains everything before implementing, sources all decisions, shows alternatives. Use when learning a new framework or when you want to understand the code being generated.
batch-api
Prepare a many-file, single-turn transform (translate, rewrite, extract) as a plan and submit it to the asynchronous, half-price DashScope Batch API; results are delivered as new files hours later. Invoke explicitly with /batch-api.