Borrowing it
Nothing to install: this file belongs to huifer/claude-code-interview. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/huifer/claude-code-interview/main/.claude/commands/interview/prep.mdgit clone --depth 1 https://github.com/huifer/claude-code-interviewWrote 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/commands/huifer/claude-code-interview/prep)<a href="https://agentmods.dev/commands/huifer/claude-code-interview/prep"><img src="https://agentmods.dev/badge/commands/huifer/claude-code-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/commands/huifer/claude-code-interview/prep"><img src="https://agentmods.dev/badge/commands/huifer/claude-code-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 | $0.00007 | $0.01093 |
| Opus 5.5 | $0.00003 | $0.00437 |
| Sonnet 5.5 | $0.00001 | $0.00219 |
| Haiku 4.5 | $0.00001 | $0.00109 |
Grade A, and why
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 2mo 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.
What it actually says
面试准备指南
为 $1 生成定制化面试准备指南。
参数说明
- company-name: 必需,目标公司名称
- interview-type: 可选,面试类型
technical- 技术面试system-design- 系统设计behavioral- 行为面试hr- HR面试- 省略则生成全面准备指南
工作流程
步骤 1: 分析公司和JD
读取 data/companies/$1.json:
- 提取关键技能
- 识别技术栈
- 了解面试流程
- 分析公司文化
步骤 2: 识别准备重点
基于JD和公司特点:
- 技术主题清单
- 优先级排序
- 时间估算
步骤 3: 推荐练习题
使用 question-generator Skill:
- 生成相关技术题
- 推荐系统设计题
- 准备行为面试题
步骤 4: 生成准备指南
创建 exports/interview-guides/$1_prep_guide.md
准备指南结构
# $1 面试准备指南
生成时间: {当前时间}
目标职位: {职位名称}
## 面试流程概览
{面试各阶段}
## 技术准备清单
### 高优先级 (必须掌握)
- [ ] {主题1} - 预计{X}小时
- [ ] {主题2} - 预计{X}小时
### 中优先级 (建议掌握)
- [ ] {主题3} - 预计{X}小时
### 低优先级 (加分项)
- [ ] {主题4} - 预计{X}小时
## 推荐练习题
### 算法和数据结构
1. {题目1} - {难度} - {频率}
2. {题目2} - {难度} - {频率}
### 系统设计
1. {题目1} - {公司常见}
2. {题目2} - {相关度}
### 行为面试 (STAR)
1. {问题1} - {能力}
2. {问题2} - {能力}
## 公司特定准备
### 技术重点
- {该公司重视的技术}
- {面试风格}
### 文化匹配
- {公司价值观}
- {行为特质}
### 已知问题
- {历年高频题}
- {该公司特色题}
## 时间规划
### 距离面试还有{X}天
**每日任务**:
- Day 1-3: {任务}
- Day 4-7: {任务}
### 每日时间分配
- 上午 (2h): {主题}
- 下午 (2h): {主题}
- 晚上 (1h): {主题}
## 学习资源
- {推荐资源}
- {练习平台}
- {参考文档}
## 模拟面试
- /interview/simulate $1 technical
- /interview/simulate $1 system-design
- /interview/simulate $1 behavioral
## 最后提醒
- {重要提示}
- {注意事项}
示例输出片段
Google 技术准备
## 高优先级技术主题
### 1. 分布式系统 (8h)
**重点**:
- CAP定理
- 一致性算法
- 分布式事务
- 数据分片
**推荐练习**:
- 设计URL短链服务
- 设计分布式缓存
- 设计分布式锁
**资源**:
- DDIA - Designing Data-Intensive Applications
- Google系统设计论文
### 2. 算法和数据结构 (6h)
**重点**:
- 图算法
- 动态规划
- 树遍历
- 哈希表
**推荐题目**:
- LRU Cache (Medium)
- Merge K Sorted Lists (Hard)
- Word Ladder (Medium)
### 3. Python深入 (4h)
**重点**:
- GIL
- 内存管理
- 装饰器
- 生成器
## Google已知问题
1. 设计一个URL缩短服务 ⭐⭐⭐
2. 实现LRU缓存 ⭐⭐⭐
3. 设计一个键值存储 ⭐⭐
4. 解释MapReduce ⭐⭐
输出文件
保存到: exports/interview-guides/{company}_prep_guide.md
下一步
- 查看准备指南
- 制定学习计划
- 开始每日练习
- 定期模拟面试
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.
- 2mo ago First seen · 178 lines · 7 tokens per session scan A 699b06de6798
prep is a command published in the GitHub repository huifer/claude-code-interview (27 stars, last pushed 10mo ago), licensed MIT. It adds 7 tokens to every session and 1,093 once invoked, about $0.0000 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 2026-08-30.
prep compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| prephuifer/claude-code-interview | Command | 27 | 10mo ago | MIT | A | 7 | Claude Code |
| learn-story-flowIntai/story-flow | Command | 16 | 2d ago | MIT | A | 10 | Claude Code |
| start-10-1minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| start-0-8minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| setup-m365cli.esminicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 8 | Claude Code |
| prep-rolefourleafai/clover-public | Command | 8 | 1mo ago | MIT | A | 0 | Claude Code |
| setup-vercel.esminicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | B | 2 | Claude Code |