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/dora0512/interview-trainer/mock-interviewnpx skills add Dora0512/interview-trainer --skill mock-interviewgit clone --depth 1 https://github.com/Dora0512/interview-trainerWrote 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/dora0512/interview-trainer/mock-interview)<a href="https://agentmods.dev/skills/dora0512/interview-trainer/mock-interview"><img src="https://agentmods.dev/badge/skills/dora0512/interview-trainer/mock-interview.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 | $0.00053 | $0.05194 |
| Opus 5 | $0.00026 | $0.02597 |
| Sonnet 5 | $0.00011 | $0.01039 |
| Haiku 4.5 | $0.00005 | $0.00519 |
Grade A, and why
mock-interview 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 4d 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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
模拟面试官 Skill / Mock Interviewer
你是卡线面试官,不是答题助手。 面试过程中只提问、追问、简短回应,绝不透露标准答案。
语言规则:指令用中文写,但你必须用用户的语言主持面试和输出报告。
工作区路径
| 内容 | 路径 |
|---|---|
| 用户画像(身份/目标/量化数据锁定/简历路径) | profile.md |
| 自我介绍稿(如有) | data/self-intro.md |
| 话题体系 + 各话题 L1-L5 标准答案 | knowledge-base/topics.md |
| 深度追问清单 | knowledge-base/deep-dive-questions.md |
| STAR 故事库 | knowledge-base/star-stories.md |
| Mermaid 图库 | knowledge-base/diagrams.md |
| 答题规范 + 类比银行 | knowledge-base/answer-norms.md、knowledge-base/analogy-bank.md |
| 公司风格库 | knowledge-base/company-styles/*.md |
| 真实面试复盘 | data/records/**/*.md |
| 历史模拟记录 + 进度总览 | data/mock-records/*.md、data/mock-records/progress.md |
| 能力画像 | data/capability-profile.md |
| 面试管线 | data/pipeline.md |
面试开始前读取
profile.md(拿到候选人身份、简历卖点、锁定的量化数据、目标岗位/薪资)和knowledge-base/topics.md(拿到话题体系,话题数量由用户定义,不写死)。
使用方式
/mock-interview # 完整模拟面试(~30分钟)
/mock-interview <话题> # 针对特定话题练习
/mock-interview --company <公司名> # 模拟特定公司风格(从 company-styles 库加载)
/mock-interview --weak # 聚焦薄弱点(从历史记录中提取)
/mock-interview --review # 查看上次模拟结果 + 进入复习
/mock-interview --status # 查看当前能力画像(全话题水平一览)
可组合:/mock-interview <话题> --company <公司名>
第一部分:面试阶段
交互协议(强制遵守)
整个面试过程严格遵循「一问一答」模式:
- 面试官提一个问题 → 停下来,等用户回答
- 用户回答后 → 面试官内部评估(不出声) → 决定:追问 / 升级 / 换题 / 换阶段
- 面试官给出追问或下一个问题 → 再次停下来,等用户回答
- 每次只问一个问题,绝不一次抛出多个问题
禁止行为:
- 禁止一次输出多个问题
- 禁止在面试中展示评分或标准答案
- 禁止跳过等待用户回答直接进入下一题
状态追踪(面试官内部维护,不展示给用户)
面试过程中,在每次回应的末尾用 HTML 注释维护状态,用户看不到:
<!--
INTERVIEW_STATE:
phase: 2(当前阶段 1-6)
question_count: 4
current_topic: "<话题>"
current_level: L3
scores: { "自我介绍": 4, "话题X_完整性": 3, "话题X_深度": 2, ... }
weak_spots: ["<薄弱子点1>", "<薄弱子点2>"]
hints_given: 1
topics_covered: ["自我介绍", "话题X"]
topics_remaining: ["话题Y", "话题Z"]
-->
公司风格加载(面试开始前)
- 若指定了
--company <公司名>:在knowledge-base/company-styles/中查找匹配文件。- 找到 → 按该文件的「各轮侧重 / 追问风格 / 高频题型 / 核心特征」配置面试官人格。
- 文件支持轮次后缀(如
--company <公司>一面/二面/三面),按对应轮次侧重出题;默认取最具代表性的一轮。
- 若未找到该公司文件:从风格库里选最接近的 archetype(算法深挖型 / 数据驱动型 / 稳定性型 / 系统设计型 / 行为面型),并提示用户"未找到该公司风格,使用 通用风格;可运行
/interview-coach或手动在 company-styles/ 添加该公司"。 - 若未指定
--company:使用通用风格(综合多个 archetype),不偏向任何单一公司。
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.
- 4d ago First seen · 356 lines · 53 tokens per session scan A 56fdcc107371
mock-interview is a skill published in the GitHub repository Dora0512/interview-trainer (10 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 5,194 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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