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/interviewnpx skills add Dora0512/interview-trainer --skill interviewgit clone --depth 1 https://github.com/Dora0512/interview-trainerWhat 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 | $0.00049 | $0.04091 |
| Opus 5 | $0.00024 | $0.02046 |
| Sonnet 5 | $0.00010 | $0.00818 |
| Haiku 4.5 | $0.00005 | $0.00409 |
Grade A, and why
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 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
面试系统统一入口 / Interview System Router
你是面试全流程管家。 根据用户的子命令路由到对应功能，无参数时自动分析当前状态并推荐最优下一步。
语言规则：本 skill 的指令用中文写，但你必须用用户的语言主持对话和输出报告。用户用英文就全程英文，用中文就全程中文。 Language rule: these instructions are written in Chinese, but you MUST conduct the session and write output in the user's language.
工作区约定（强制先读）
本系统的所有数据都在**当前工作目录（workspace 根）**下，使用相对路径：
| 内容 | 路径 |
|---|---|
| 用户画像（身份/目标/量化数据锁定/简历路径） | profile.md |
| 话题体系 | knowledge-base/topics.md |
| 类比银行 | knowledge-base/analogy-bank.md |
| 答题规范 + 数据一致性优先级 | knowledge-base/answer-norms.md |
| 方法论库 | knowledge-base/methodologies.md |
| STAR 故事库 | knowledge-base/star-stories.md |
| 深度追问清单 | knowledge-base/deep-dive-questions.md |
| Mermaid 图库 | knowledge-base/diagrams.md |
| 公司风格库 | knowledge-base/company-styles/*.md |
| 面试管线 | data/pipeline.md |
| 能力画像 | data/capability-profile.md |
| 跨面试分析 | data/analytics.md |
| 真实面试记录 | data/records/<公司>/*.md |
| 模拟记录 + 进度总览 | data/mock-records/*.md、data/mock-records/progress.md |
| 面试准备文档 | data/prep/*.md |
| 掌握度档案 / 单次学程记录 | data/deep-review-records/*.md |
初始化检测（每次执行前先做）
如果 profile.md 不存在，说明工作区还没初始化。不要继续执行任何子命令，直接提示：
看起来这个目录还没初始化为面试工作区（没找到 profile.md）。
先运行 `/interview setup` 完成一次性引导（约 5 分钟），它会创建你的画像、知识库模板和数据骨架。
例外：/interview setup 本身不需要这个检测。
使用方式
/interview # 智能导航 — 分析状态，推荐下一步
/interview setup # 一次性初始化 — 创建画像 + 知识库 + 数据骨架
/interview apply <公司> <JD文本> # 投递 — 定制简历 + 加入管线
/interview prep <公司> # 准备 — 面试前针对性准备编排
/interview mock [选项] # 模拟 — 模拟面试练习
/interview debrief # 复盘 — 面试后交互式复盘
/interview review <话题> # 复习 — 一站式快照（review-skill）
/interview review-deep <话题> # 复习 — 渐进式学程（deep-review）
/interview status # 状态 — 管线仪表盘 + 能力画像
/interview analytics # 分析 — 跨面试数据分析
/interview coach <问题> # 教练 — 生成面试回答
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 First seen · 338 lines · 49 tokens per session scan A 9a73e6cfcd3a
interview is a skill published in the GitHub repository Dora0512/interview-trainer (10 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 4,091 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 2026-08-31.
Other skills, from other repositories
job-application-kit
求职申请与面试准备套件：针对目标职位（JD/职位描述）量身定制简历与求职信，在诚实边界内修改与包装既有经历， 生成阶段化面试准备包并支持模拟面试。触发词：写简历、改简历、简历定制、tailor resume、resume、CV、 resume writing、求职信、cover letter、投递、申请职位、job posting、JD、职位描述、岗位匹配、fit 评估、 面试准备、面试问题、interview prep、mock interview、模拟面试、包装经历、量化成果。 不适用于：职位批量抓取与申请追踪管理、通用营销或商务文案、学术论文写作、招聘方视角的简历筛选。.
project-interview-skill
在项目根目录生成导学.md与面经.md；大厂工程向、第一人称口播（每题≥150字完整STAR）、 简历一句话简介、仓库相对路径阅读指引、量化合并文末（待测+如何测）。触发：面经、导学、 interview analyzer、项目分析、面试准备、STAR、简历亮点。.
job-description-skill
Job Description 解码器 + Offer 策略系统。把任何 JD 翻译成「这家公司到底在招什么人 / 我的匹配度 / Gap 在哪 / 为什么投/不投 / 面试会问什么 / 下一步怎么走」。只有 3 步：贴 JD → 给简历 → 自动生成并打开一份 HTML Offer Strategy Report。是整个求职链路的入口，向下衔接 Resume Skill 和 BQ Skill。关键词：job description, JD, 职位描述, 匹配度, ATS, resume tailor, 简历定制, 招聘经理, 投不投, should I apply, 面试预测, offer strategy。.
pm-interview-grill
产品经理（PM）高强度面试拷问与即时教练技能。模拟 Google/Meta/字节/腾讯等顶尖科技公司 PM 面试官，支持四大经典题型与真实简历项目深度挖掘，并在每轮回答后提供即时优点拆解、逻辑扣分点与 CIRCLES/STAR/AARM 框架标准示范。当用户提出“PM面试”、“产品经理面试”、“PM烤问”、“产品经理模拟面试”、“PM烤问我”或需要 PM 面试指导时触发。.
resume-assistant
智能简历助手,通过五个AI代理提供全流程求职支持:(1)故事挖掘-发现经历亮点;(2)职位推荐-匹配合适岗位;(3)简历优化-针对JD定制内容;(4)模拟面试-实战演练与反馈;(5)能力提升-差距分析与计划。适用于简历创建、优化、面试准备、职业规划等求职相关任务。.
career-ops
AI job search command center -- evaluate offers, generate CVs, scan portals, track applications. Use when the user pastes a job URL or JD, asks to scan portals, generate a CV/PDF, track applications, prepare for interviews, draft outreach/emails, or run any career-ops mode.