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/generategit 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 | $0.00011 | $0.00611 |
| Opus 5 | $0.00005 | $0.00305 |
| Sonnet 5 | $0.00002 | $0.00122 |
| Haiku 4.5 | $0.00001 | $0.00061 |
Grade A, and why
generate 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
生成Markdown简历
将简历数据转换为格式化的Markdown文档。
参数说明
- variant-name: 可选，指定要生成的简历变体
- 省略或使用 "base": 生成基础简历
- 使用公司名（如 "google"）: 生成该公司定制简历
工作流程
步骤 1: 读取简历数据
- 如果指定了变体，读取
data/resume/variants/{variant}.json - 否则读取
data/resume/base.json
步骤 2: 应用模板
使用 templates/resume_template.md 模板生成简历
步骤 3: 生成Markdown
创建 exports/resumes/{name}_resume.md 文件
步骤 4: 输出结果
显示生成的文件路径和预览
简历模板结构
# {姓名}
{联系方式}
## 专业总结
{summary}
## 技能
### 编程语言
{skills.programming_languages}
### 框架和库
{skills.frameworks}
### 数据库
{skills.databases}
### 工具
{skills.tools}
### 技术概念
{skills.concepts}
## 工作经历
### {职位} | {公司} | {时间}
{工作总结}
**成就**:
- {成就1}
- {成就2}
**技术栈**: {technologies}
## 项目经验
### {项目名称} | {角色}
{项目描述}
**成就**:
- {成就1}
- {成就2}
**技术**: {technologies}
**链接**: {url}
## 教育背景
### {学位} | {学校} | {时间}
**GPA**: {gpa}
**荣誉**: {honors}
**相关课程**: {courses}
## 认证证书
{certifications}
## 语言能力
{languages}
输出示例
✅ 简历已生成: exports/resumes/google_resume.md
📄 文件内容预览:
# 张三
📧 [email protected] | 📱 +86 138-0000-0000
📍 北京, 中国
🔗 linkedin.com/in/zhangsan | 💻 github.com/zhangsan
## 专业总结
资深软件工程师，专注于分布式系统和机器学习...
...
下一步操作
- 查看生成的Markdown文件
- 可以手动编辑微调
- 转换为PDF（使用pandoc或其他工具）
- 用于投递简历
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 · 114 lines · 11 tokens per session scan A af99ba1fbc41
generate is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 11 tokens to every session and 611 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.