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 kyoungbinkim/give-me-job --skill interview-prepgit clone --depth 1 https://github.com/kyoungbinkim/give-me-jobWrote 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/kyoungbinkim/give-me-job/interview-prep)<a href="https://agentmods.dev/skills/kyoungbinkim/give-me-job/interview-prep"><img src="https://agentmods.dev/badge/skills/kyoungbinkim/give-me-job/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/skills/kyoungbinkim/give-me-job/interview-prep"><img src="https://agentmods.dev/badge/skills/kyoungbinkim/give-me-job/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 | $0.00065 | $0.01125 |
| Opus 5 | $0.00032 | $0.00562 |
| Sonnet 5 | $0.00013 | $0.00225 |
| Haiku 4.5 | $0.00006 | $0.00112 |
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 2d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Prep
Use this skill to turn a cover letter package into interview preparation that the applicant can defend from resume.md evidence.
Trigger
Use this skill after cover-letter-draft.md or cover-letter-final.md exists and an evidence map is available.
Use it when the user asks for 면접 준비, 꼬리질문, 예상 질문, 답변 포인트, 모의면접, 면접 복기, 결과 분석, or interview defense based on a Korean application package.
Do Not Trigger
Do not use this skill before JD analysis and resume evidence exist.
Do not invent missing answers, metrics, tools, or company context. If a claim cannot be defended from resume.md, mark it as missing evidence.
Autonomy Level
DoF: LOW
Follow the input evidence. Generate questions and answer points only from the JD, cover letter, and resume.md. Do not add new achievements or unsupported explanation.
Permitted inferences:
- Likely interviewer follow-up questions from a specific cover-letter claim.
- Risk level from the strength of the mapped resume evidence.
Prohibited inferences:
- Do not infer unlisted tools, metrics, responsibilities, awards, or business impact.
- Do not turn weak evidence into a confident answer point.
Input Contract
Required context:
resume.md: structured career evidence.applications/<company-role>/jd-analysis.md: role requirements and evaluation criteria.applications/<company-role>/evidence-map.md: claim-to-evidence mapping.applications/<company-role>/cover-letter-final.mdorcover-letter-draft.md: answer text to defend.
Optional context:
applications/<company-role>/hr-review.md: blocker and warning context.applications/<company-role>/company-values.md: optional company values context.
Required parameters:
company: target company name.role: target role title.
Outputs produced:
applications/<company-role>/interview-prep.md
Workflow
- Read the cover letter and split it into core claims.
- Match each claim to
evidence-map.mdandresume.md. - Generate 2-3 follow-up questions for each core claim.
- Add verification questions for metrics, tools, role scope, collaboration, failure handling, and JD fit when relevant.
- For any claim whose strength rests on a decision, add a question about the alternatives considered and why they were rejected. Interviewers probe the reasoning behind a result more often than the result itself.
- For any 실패/성장과정 claim, add a question about what the candidate changed afterward and where that change applied later. An answer that stops at the lesson is the common weak point.
- Write answer points using only supported evidence.
- Mark missing or thin evidence clearly.
- Add a short preparation checklist for manual review before interview.
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.
- 2d ago Changed · +24 lines d330aa58376d
- 11d ago First seen · 112 lines · 65 tokens per session scan A 2ed38630f9ab
interview-prep is a skill published in the GitHub repository kyoungbinkim/give-me-job (5 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 1,125 once invoked, about $0.0003 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 2026-08-31.
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