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/variantgit 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 | $0.00012 | $0.00555 |
| Opus 5 | $0.00006 | $0.00278 |
| Sonnet 5 | $0.00002 | $0.00111 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
variant 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
创建简历变体
为 $ARGUMENTS 创建定制化简历变体。
工作流程
步骤 1: 检查公司信息
首先检查是否已添加该公司的JD信息。
如果 data/companies/$ARGUMENTS.json 不存在，提示用户：
未找到 $ARGUMENTS 的公司信息。
请先运行 /company/add $ARGUMENTS 添加公司信息和JD。
步骤 2: 使用 jd-analyzer Skill
调用 jd-analyzer Skill 分析该公司的JD：
- 提取必需技能和优先技能
- 识别关键关键词
- 分析公司文化
- 评估与基础简历的匹配度
步骤 3: 创建简历变体
基于JD分析结果，创建 data/resume/variants/$ARGUMENTS.json：
{
"variant_id": "$ARGUMENTS",
"parent_resume": "base.json",
"created_at": "当前时间",
"updated_at": "当前时间",
"target_company": "$ARGUMENTS",
"optimizations": {
"highlighted_skills": [...],
"emphasized_projects": [...],
"tailored_summary": "...",
"keywords_to_emphasize": [...],
"reordered_sections": [...]
}
}
步骤 4: 优化建议
提供详细的优化建议，包括：
- 哪些技能应该突出显示
- 哪些项目经验应该强调
- Summary如何调整
- 成就描述如何重写
- 建议的关键词插入位置
步骤 5: 生成报告
输出匹配度分析报告：
# $ARGUMENTS 简历匹配度分析
## 匹配度评分: XX/100
### ✅ 完全匹配的技能
- ...
### ⚠️ 部分匹配的技能
- ...
### ❌ 缺失的关键技能
- ...
## 优化建议
1. ...
2. ...
3. ...
## 下一步
使用 /resume/generate $ARGUMENTS 生成Markdown简历
注意事项
- 确保公司信息文件已存在
- JD分析需要 jd-analyzer Skill
- 简历变体不会修改基础简历
- 可以为同一公司创建多个变体（不同职位）
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 · 87 lines · 12 tokens per session scan A a249a9cc7ddf
variant is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 12 tokens to every session and 555 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.