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/viewgit 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 | $0.00009 | $0.00923 |
| Opus 5 | $0.00005 | $0.00462 |
| Sonnet 5 | $0.00002 | $0.00185 |
| Haiku 4.5 | $0.00001 | $0.00092 |
Grade A, and why
view 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.
What it actually says
查看公司详情
显示 $ARGUMENTS 的详细信息和求职进展。
显示内容
1. 公司基本信息
- 公司名称、行业、规模
- 公司官网、总部
- 公司类型
2. 目标职位
- 职位名称和级别
- 工作地点和团队
- 职位描述摘要
3. JD分析结果
技能要求
- 必需技能列表
- 优先技能列表
- 加分项
匹配度分析
- ✅ 完全匹配的技能
- ⚠️ 部分匹配的技能
- ❌ 缺失的技能
总体匹配度
- 评分 (0-100)
- 匹配度等级
- 改进建议
4. 申请进度
- 当前状态
- 当前阶段
- 已完成的阶段
- 下一步行动
5. 面试安排
- 已安排的面试
- 面试类型和格式
- 面试官信息（如有）
- 时间和平台
6. 研究笔记
- 公司文化
- 面试流程
- 已知面试题
- 准备提示
7. 薪资范围
- 基本工资范围
- 奖金比例
- 股票/RSU
示例输出
# Google 详细信息
## 公司信息
**行业**: Technology
**规模**: 100,000+
**类型**: Public
**总部**: Mountain View, CA
**官网**: google.com
## 目标职位
**职位**: Software Engineer III
**级别**: L4
**地点**: Beijing, China
**团队**: Cloud Platform
**状态**: interviewing
## JD分析
### 必需技能
- Python ✓ (expert)
- Java ✓ (intermediate)
- Distributed Systems ✓ (advanced)
- Cloud Computing ✓ (advanced)
### 优先技能
- Kubernetes ⚠️ (基础)
- Machine Learning ✓ (intermediate)
### 缺失技能
- C++ ❌ (需要学习)
### 匹配度: 75/100 (良好)
## 申请进度
**当前状态**: interviewing
**当前阶段**: Technical Round (2/5)
### 已完成
✅ Resume Screen - 2025-01-06
✅ Online Assessment - 2025-01-08
### 进行中
🔄 Technical Round - 已安排 2025-01-15
### 待安排
⏳ System Design
⏳ Behavioral Interview
⏳ HR Round
## 面试安排
**2025-01-15 14:00**
- 类型: Technical Interview
- 格式: Online (Google Meet)
- 时长: 45分钟
- 准备: 算法、数据结构、编码
## 研究笔记
**公司文化**:
- 重视创新和技术卓越
- "Googley"文化（好奇、学习、同理心）
- 用户至上
**面试流程**:
1. 简历筛选
2. 在线测试
3. 技术面试（2轮）
4. 系统设计（1轮）
5. 行为面试（1轮）
**已知问题**:
- 设计URL短链服务
- 实现LRU缓存
- 二叉树遍历
**准备提示**:
- Google强调可扩展性和可靠性
- 重视代码质量和可测试性
- 系统设计要考虑数据一致性
## 薪资范围
- Base: 300k-500k RMB/year
- Bonus: 15-20%
- Stock: RSU 4年
## 快速操作
- /resume/variant google - 创建定制简历
- /interview/prep google - 生成准备指南
- /interview/simulate google technical - 模拟面试
下一步建议
根据当前状态提供具体建议：
- 需要准备什么
- 优先级排序
- 时间安排建议
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 · 160 lines · 9 tokens per session scan A 9f4242274735
view is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 9 tokens to every session and 923 once invoked, about $0.0000 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.