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 commands/huifer/claude-code-interview/setupgit clone --depth 1 https://github.com/huifer/claude-code-interviewWhat 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 | $0.00028 | $0.02945 |
| Opus 5 | $0.00014 | $0.01473 |
| Sonnet 5 | $0.00006 | $0.00589 |
| Haiku 4.5 | $0.00003 | $0.00295 |
Grade A, and why
setup 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 — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
系统初始化向导
欢迎使用 Claude Code Interview System!
本向导将引导您完成初始配置,让您快速开始使用系统管理求职和面试准备。
配置步骤
步骤 1: 创建基础数据目录结构
确保以下目录存在:
data/resume/- 简历数据data/companies/- 公司和职位数据data/interviews/- 面试记录data/questions/- 问题库data/analytics/- 进度追踪data/schemas/- JSON Schema 验证文件exports/- 导出文件templates/- 简历模板
步骤 2: 配置个人信息
询问以下信息并保存到配置文件:
必填信息:
- 姓名 - 用于生成个性化文档
- 邮箱 - 主要联系方式
- 电话 - 备用联系方式
可选信息:
- 所在地 - 城市, 国家/地区
- LinkedIn - LinkedIn个人资料链接
- GitHub - GitHub个人资料链接
- 个人网站 - 个人作品集或博客
步骤 3: 配置求职偏好
目标职位
-
目标职位类型 (可多选):
- 软件工程师 (Software Engineer)
- 前端工程师 (Frontend Engineer)
- 后端工程师 (Backend Engineer)
- 全栈工程师 (Full Stack Engineer)
- 系统架构师 (System Architect)
- 机器学习工程师 (ML Engineer)
- 数据工程师 (Data Engineer)
- DevOps工程师 (DevOps Engineer)
- 产品经理 (Product Manager)
- 其他 (请说明)
-
目标级别 (可多选):
- Junior (L1-L2)
- Mid-level (L3-L4)
- Senior (L5)
- Staff (L6)
- Principal (L7+)
-
目标地点 (可多选):
- 具体城市列表
- 是否接受远程工作
- 是否愿意 relocate
-
目标公司类型 (可多选):
- Big Tech (Google, Meta, Amazon, Microsoft, Apple)
- 独角兽创业公司
- 成长型创业公司
- 中大型公司
- 不限
薪资期望
- 期望薪资范围:
- 基础年薪范围 (例如: $150k - $200k)
- 期望的一级股票/期权
- 期望的签约奖金
- 期望的总薪酬
时间安排
- 求职时间安排:
- 开始求职日期
- 期望入职日期
- 每周可投入小时数
- 是否在职求职 (On-site job search)
步骤 4: 导入或创建简历
选项 A: 导入现有简历
如果已有简历,运行:
/resume/import <resume-file-path>
选项 B: 创建新简历
- 引导用户输入个人信息
- 逐项填写:
- 专业总结
- 技能列表
- 工作经历
- 项目经验
- 教育背景
- 认证证书
- 保存到
data/resume/base.json
步骤 5: 初始化分析数据
创建 data/analytics/progress.json:
{
"user_id": "generated-uuid",
"period": {
"start": null,
"end": null
},
"overview": {
"days_spent": 0,
"total_hours": 0,
"average_hours_per_day": 0
},
"practice_statistics": {
"technical_questions_completed": 0,
"system_design_questions_completed": 0,
"behavioral_questions_practiced": 0,
"mock_interviews_completed": 0
},
"skill_proficiency": {},
"company_progress": {},
"weak_areas": [],
"weekly_goals": []
}
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 · 418 lines · 28 tokens per session scan A 332a8acc79e5
setup is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 28 tokens to every session and 2,945 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.