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/addgit 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 | $0.00015 | $0.02283 |
| Opus 5 | $0.00008 | $0.01141 |
| Sonnet 5 | $0.00003 | $0.00457 |
| Haiku 4.5 | $0.00002 | $0.00228 |
Grade A, and why
add 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- jd — 95% identical, 367 lines differ
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
添加公司信息
添加 $ARGUMENTS 到您的求职列表,完整保存职位描述(JD)信息。
收集步骤
1. 公司基本信息
请提供以下信息:
必填:
- 公司名称: $ARGUMENTS
- 行业(如:Technology, Finance, Healthcare)
- 公司官网
可选:
- 总部地点
- 公司规模(如:100-500, 1000-5000, 10000+)
- 公司类型(如:Public, Private, Startup)
2. 目标职位信息
请提供您申请的职位信息:
- 职位ID(自动生成或手动指定,如:pos_001)
- 职位名称(如:Software Engineer III)
- 职位级别(如:L4, Senior, Principal)
- 工作地点
- 团队/部门
- 职位状态(如:preparing, applied, interviewing)
3. 职位描述(JD)- 重要!⭐
这是最重要的部分!我们将完整保存JD文本。
JD信息收集
请提供以下JD信息:
完整JD文本 (必填): 您可以通过以下方式提供:
- 直接粘贴 - 复制完整JD文本粘贴到这里
- 上传文件 - 提供包含JD的文件路径
- JD链接 - 提供在线JD链接,我将帮您读取
JD来源 (可选):
- 公司官网
- 招聘网站(如:拉勾、Boss直聘)
- 内推
- 其他
JD文本应包含:
- ✅ 职位概述
- ✅ 职责描述
- ✅ 技能要求(必需和优先)
- ✅ 资格要求(教育和经验)
- ✅ 加分项
- ✅ 福利待遇(可选)
JD保存方式
系统将:
- 保存完整JD原文 到
raw_text字段 - 记录JD来源 到
description_source字段 - 记录抓取时间 到
scraped_at字段 - 使用 jd-analyzer Skill 分析JD
- 提取结构化信息 到
structured_analysis - 评估匹配度 到
skill_match_analysis
4. 自动分析
收集完信息后,我将:
- 创建
data/companies/$ARGUMENTS.json文件 - 保存完整JD原文
- 使用 jd-analyzer Skill 分析JD
- 提取关键技能和要求
- 评估与您简历的匹配度
- 生成结构化分析报告
5. 输出报告
✅ 公司信息已保存: data/companies/$ARGUMENTS.json
📄 JD信息已保存:
- JD来源: LinkedIn
- 保存时间: 2025-01-09 14:30:00
- JD长度: 2,456 字符
📊 JD分析结果
## 关键技能
**必需** (7项):
- Python (expert)
- Distributed Systems (advanced)
- Cloud Computing (advanced)
- ...
**优先** (4项):
- Kubernetes (基础)
- Machine Learning (中级)
- ...
**加分** (3项):
- 开源贡献
- 技术博客
- ...
## 匹配度分析
✅ 完全匹配: 7项
⚠️ 部分匹配: 3项
❌ 缺失: 2项
**总体匹配度**: 75/100
**评估**:
- 技术栈匹配度高
- 需要加强Kubernetes
- 建议学习C++基础
## 下一步建议
1. /resume/variant $ARGUMENTS - 创建定制简历
2. /interview/prep $ARGUMENTS - 生成面试准备指南
3. /question/generate $ARGUMENTS - 生成面试题库
JD信息存储结构
保存的公司JSON将包含完整的JD信息:
{
"company_id": "google",
"basic_info": {...},
"target_positions": [
{
"position_id": "pos_001",
"title": "Software Engineer III",
"level": "L4",
"location": "Beijing, China",
"team": "Cloud Platform",
"status": "applied"
}
],
"job_descriptions": {
"pos_001": {
"title": "Software Engineer III, Cloud Platform",
"description_source": "https://linkedin.com/jobs/...",
"scraped_at": "2025-01-09T14:30:00Z",
"raw_text": "Google is looking for software engineers...
Minimum qualifications:
- Bachelor's degree in Computer Science or equivalent
- 3 years of experience in software development
with knowledge of one or more of the following: Python, Java, C++
...
Preferred qualifications:
- Master's degree or PhD
- 5 years of experience in distributed systems
...",
"structured_analysis": {
"required_skills": [
"Python",
"Java",
"C++",
"Distributed Systems"
],
"preferred_skills": [
"Kubernetes",
"Machine Learning",
"Data Engineering"
],
"responsibilities": [
"Design, develop, test, deploy, maintain and improve software",
"Manage individual project priorities, deadlines and deliverables"
],
"minimum_qualifications": [
"Bachelor's degree in Computer Science or equivalent",
"3 years of experience in software development"
],
"preferred_qualifications": [
"Master's degree or PhD",
"5 years of experience in distributed systems"
]
},
"skill_match_analysis": {
"matched_skills": [
{"skill": "Python", "proficiency": "expert"},
{"skill": "Distributed Systems", "proficiency": "advanced"}
],
"partial_match_skills": [
{"skill": "Java", "proficiency": "intermediate", "required": "advanced"}
],
"missing_skills": [
{"skill": "C++", "action": "需要学习基础"}
]
}
}
}
}
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 · 330 lines · 15 tokens per session scan A 0d343a43b797
add is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 15 tokens to every session and 2,283 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.