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/simulategit 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 | $0.00008 | $0.01502 |
| Opus 5 | $0.00004 | $0.00751 |
| Sonnet 5 | $0.00002 | $0.00300 |
| Haiku 4.5 | $0.00001 | $0.00150 |
Grade A, and why
simulate 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
模拟面试
启动 $1 的 $2 模拟面试。
参数说明
- company-name: 必需,目标公司名称
- interview-type: 必需,面试类型
technical- 技术面试(编码)system-design- 系统设计behavioral- 行为面试hr- HR面试onsite- 完整onsite模拟
- duration: 可选,面试时长(分钟),默认45
工作流程
步骤 1: 面试准备
读取公司和用户信息:
- 公司和职位信息
- JD分析结果
- 用户简历和技能
- 历史面试记录
步骤 2: 面试设置
- 确认面试类型和时长
- 说明面试规则
- 设置计时器
步骤 3: 开始面试
使用 interview-coach Skill 进行模拟面试:
- 扮演面试官
- 提问并追问
- 记录回答
- 计时提醒
步骤 4: 面试评估
- 分析回答质量
- 识别优势和不足
- 提供改进建议
- 评分(1-5分制)
步骤 5: 保存记录
创建 data/interviews/{date}_{company}_{type}.json
面试类型详解
Technical Interview (技术面试)
形式: 编程题 时长: 45分钟 流程:
- 介绍问题 (2分钟)
- 讨论方案 (5分钟)
- 编写代码 (20分钟)
- 测试优化 (10分钟)
- 追问讨论 (8分钟)
评估维度:
- 问题理解 (5分)
- 方案设计 (5分)
- 代码质量 (5分)
- 优化能力 (5分)
- 沟通表达 (5分)
System Design Interview (系统设计)
形式: 架构设计 时长: 45-60分钟 流程:
- 理解需求 (5分钟)
- 高层设计 (10分钟)
- 详细设计 (20分钟)
- 讨论trade-offs (10分钟)
- 总结 (5分钟)
评估维度:
- 需求理解 (5分)
- 架构设计 (5分)
- 可扩展性 (5分)
- 可靠性 (5分)
- 讨论深度 (5分)
Behavioral Interview (行为面试)
形式: STAR问题 时长: 30-45分钟 流程:
- 提问 (每个问题3-5分钟)
- 倾听回答
- 追问细节
- 评估反馈
评估维度:
- STAR结构 (5分)
- 故事相关性 (5分)
- 成果量化 (5分)
- 沟通清晰 (5分)
- 文化匹配 (5分)
HR Interview (HR面试)
形式: 问答 时长: 30分钟 流程:
- 自我介绍 (2分钟)
- 职业规划 (5分钟)
- 为什么选我们 (5分钟)
- 薪资期望 (5分钟)
- 其他问题 (10分钟)
- 反向提问 (5分钟)
面试记录格式
{
"interview_id": "int_001",
"company_id": "$1",
"position_id": "...",
"interview_date": "2025-01-09T14:00:00Z",
"interview_type": "$2",
"round": 1,
"duration_minutes": $ARGUMENTS,
"questions_asked": [
{
"question_id": "q_001",
"type": "coding|system_design|behavioral",
"question": "问题描述",
"difficulty": "medium",
"time_allocated": 20,
"user_response": {
"approach": "解题思路",
"code_written": true,
"time_taken": 18
},
"interviewer_feedback": "代码结构清晰,但可以进一步优化",
"rating": 4
}
],
"overall_feedback": {
"strengths": ["编码能力强", "沟通好"],
"areas_for_improvement": ["需要加强DP"],
"next_steps": ["练习更多DP题"]
},
"overall_rating": 3.8
}
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 · 207 lines · 8 tokens per session scan A 959bc6603061
simulate is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 8 tokens to every session and 1,502 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.