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 skills add Dora0512/interview-trainer --skill resume-tailorgit clone --depth 1 https://github.com/Dora0512/interview-trainerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dora0512/interview-trainer/resume-tailor)<a href="https://agentmods.dev/skills/dora0512/interview-trainer/resume-tailor"><img src="https://agentmods.dev/badge/skills/dora0512/interview-trainer/resume-tailor.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00045 | $0.01561 |
| Opus 5 | $0.00023 | $0.00781 |
| Sonnet 5 | $0.00009 | $0.00312 |
| Haiku 4.5 | $0.00005 | $0.00156 |
Grade A, and why
resume-tailor 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 8d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
简历定制优化 Skill / Resume Tailor
根据目标公司 JD，对基础简历进行针对性优化，输出匹配分析报告 + 定制简历。
语言规则：指令用中文写，但用用户的语言（与简历语言一致）输出。
使用方式
/resume-tailor <JD文本> # 直接粘贴职位描述
/resume-tailor --url <招聘链接> # 抓取招聘页面提取 JD
/resume-tailor --company <公司> --role <岗位> # 联网搜索该公司典型 JD
/resume-tailor --list # 查看已生成的定制简历索引
可组合：/resume-tailor --company <公司> --role <岗位> --focus <方向>
参数解析规则（强制）
按优先级判断输入类型：--list → 索引查看；--url → URL 抓取；--company → 联网搜索；否则 → 全部参数视为 JD 文本直接解析。
--focus：可选，指定优化侧重（如 --focus AI 额外突出 AI 经验）。
工作区路径
| 内容 | 路径 |
|---|---|
| 用户画像（含基础简历路径 + 定制简历目录） | profile.md |
| 基础简历 | profile.md 的 resume_path 指向的文件 |
| STAR 故事库 | knowledge-base/star-stories.md |
| 话题/技能体系 | knowledge-base/topics.md |
| 公司特化策略（如有） | knowledge-base/company-styles/*.md |
| 定制简历索引 | profile.md 的 resume_dir/定制版 README（自动维护） |
| 面试管线 | data/pipeline.md |
简历路径和定制版输出目录都从
profile.md读取，不写死。
核心流程（5 步，强制按顺序执行）
Step 1：JD 解析
方式 A 直接文本；方式 B 用 WebFetch 抓 URL；方式 C 用 WebSearch 搜 <公司> <岗位> 招聘 JD 要求。解析输出结构化 JD 画像：
## JD 画像
- **公司** / **岗位** / **职级推断** / **团队/业务**
### 硬性技术要求（必须匹配）
### 职责要求
### 加分项
### ATS 关键词清单
Step 2：匹配度分析（百分制）
读基础简历，逐维度对比：
## 匹配度评分报告
### 总分：XX / 100
| 维度 | 权重 | 得分 | 说明 |
| 技术栈覆盖率 | 30% | XX/30 | JD 要求 N 项，简历覆盖 M 项 |
| 经验年限匹配 | 10% | XX/10 | |
| 项目相关性 | 25% | XX/25 | |
| 关键词密度 | 15% | XX/15 | |
| 成果量化度 | 10% | XX/10 | |
| 差异化竞争力 | 10% | XX/10 | |
### 维度详细分析
#### 技术栈覆盖 | JD 要求 | 简历状态 | 匹配 |
#### 关键词覆盖 | ATS 关键词 | 简历出现 | 建议 |
Step 3：优化建议报告
三栏对比（🔴 P0 必须改 / 🟡 P1 建议改 / 🟢 P2 可选）+ ATS 关键词补充清单 + STAR 成果重写建议。
STAR 故事库联动：读 star-stories.md，JD 强调的方向找对应故事关键细节，建议融入简历。
然后使用 AskUserQuestion 询问用户：1. 确认全部建议直接生成 / 2. 逐条确认后生成 / 3. 只看建议不生成。 等用户确认后进入 Step 4。
Step 4：生成优化简历
修改原则（红线，不可违反）：
- 不编造经历 — 只调整措辞、排序、重点，不虚构项目或技术。每条描述必须在基础简历中有出处
- 保持量化数据一致 — 所有数字必须与基础简历 /
profile.md锁定数据完全一致 - 自然融入关键词 — 不堆砌，在合适上下文自然使用
- 突出相关性 — 按与 JD 相关度重排项目成果
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.
- 8d ago First seen · 109 lines · 45 tokens per session scan A 31af1e76ce07
resume-tailor is a skill published in the GitHub repository Dora0512/interview-trainer (10 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 1,561 once invoked, about $0.0002 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 2026-08-31.
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