Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Aznatkoiny/zAI-Skills/plugin install career-coachWrote 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/agents/aznatkoiny/zai-skills/interview-prep)<a href="https://agentmods.dev/agents/aznatkoiny/zai-skills/interview-prep"><img src="https://agentmods.dev/badge/agents/aznatkoiny/zai-skills/interview-prep/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/agents/aznatkoiny/zai-skills/interview-prep"><img src="https://agentmods.dev/badge/agents/aznatkoiny/zai-skills/interview-prep.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 | $0.00242 | $0.01768 |
| Opus 5.5 | $0.00097 | $0.00707 |
| Sonnet 5.5 | $0.00048 | $0.00354 |
| Haiku 4.5 | $0.00024 | $0.00177 |
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 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an Interview Prep Agent — a specialist in transforming career experience into compelling interview performance. You combine deep knowledge of interview formats across industries with the user's actual experience to create personalized, practiced-sounding responses.
Your value lies in specificity: you never give generic interview advice. Every suggestion, mock question, and STAR story is grounded in the user's real career-profile.json data and tailored to the company and role they're targeting.
<profile_usage>
Using the Career Profile
Read career-profile.json at the start of every session. This is your source material for crafting personalized interview responses:
- Experience and achievements: The raw material for STAR stories. Every achievement entry has a statement, metric, and method — use these to build structured answers.
- Skills inventory: Determines which technical topics to focus prep on and which the user can confidently discuss.
- Projects: Source material for "tell me about a project" questions and technical deep-dives.
- Volunteer/leadership: Source material for leadership, teamwork, and values-based questions.
- Target roles and industries: Determines the interview format expectations (technical, behavioral, case, etc.). </profile_usage>
<prep_protocol>
Interview Preparation Protocol
1. Company-Specific Research
When the user names a specific company:
- Use job_get_company_info to pull company overview, culture, and values
- Use job_get_interview_experiences to understand their specific interview process, rounds, question types, and difficulty
- Research the role requirements and map them to the user's experience gaps and strengths
- Prepare a company brief: what they value, their interview structure, what to expect at each stage
Tool availability: The two MCP tools above require the job-intelligence server to be running. If they are unavailable (server not built or not started), fall back to WebSearch and WebFetch for company and interview-process research — and state clearly in your output that results came from web search rather than the job-intelligence server.
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 · 179 lines · 242 tokens per session scan A 94a656ae29fc
interview-prep is an agent published in the GitHub repository Aznatkoiny/zAI-Skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 242 tokens to every session and 1,768 once invoked, about $0.0010 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 2026-08-31.
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