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 ssmurfgg04-gif/context-m --skill jd-resume-tailorgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/jd-resume-tailor)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/jd-resume-tailor"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/jd-resume-tailor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/jd-resume-tailor"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/jd-resume-tailor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00183 | $0.01675 |
| Opus 5 | $0.00092 | $0.00838 |
| Sonnet 5 | $0.00037 | $0.00335 |
| Haiku 4.5 | $0.00018 | $0.00168 |
Grade A, and why
jd-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 9d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
JD ⇄ Resume Tailor（JD 拆解 + 简历定向改写）
这个 skill 的边界很窄：只解决"已有 JD + 已有简历，要改一份命中率最高的版本"。
不做的事：
- 写新简历（去
resume-builder） - 找方向 / 推荐岗位（去
job-intent-tracker） - 出面试题（去
interview-prep）
何时触发
强信号：
- 用户给了 JD 链接 / JD 文本 + 一份简历 → 必触发
- "针对这个岗位帮我改简历"
- "对照一下这个 JD"
- "我想投 X 公司的 Y 岗，帮我看简历"
- "做一份定向版"
弱信号（先确认）：
- 只给了 JD 没有简历 → 问"你的简历方便发我看一下吗？没有的话，我可以先帮你从零做一份（resume-builder）"
- 只给了简历说"改简历" → 问"是针对哪个 JD 改？没有 JD 就用 resume-builder 通用优化"
工作流
Step 1: 解析 JD
输入可能是：
- 纯文本（用户粘贴）
- 链接（不要自动 fetch，提醒用户复制 JD 文本进来；若用户授权 fetch，使用 web_fetch）
- 截图（用 OCR / 视觉识别，让用户确认抽取结果）
- doc/pdf 文件
调用脚本：
python scripts/parse_jd.py --jd-file <jd.txt> --out jd_parsed.json
脚本会从 JD 抽出：
- 硬技能 must-have（"必须" / "要求" / "至少 X 年" 等强信号词后面的技能）
- 硬技能 nice-to-have（"加分" / "优先" / "熟悉者优先" 等弱信号）
- 软技能信号（沟通 / 推动 / 跨部门 / 抗压 等）
- 职责动词 + 对象（"负责 X" / "搭建 Y" / "推动 Z"）
- 特殊要求（出差 / 学历 / 证书 / 语言 / 城市）
把结果展示给用户，让用户确认 / 修正抽取是否准确（关键 must-have 不能漏）。
Step 2: 解析简历
输入：用户上传的简历文件（.pdf / .docx / .md / .txt）。
调用对应 skill 解析：
- pdf → pdf skill
- docx → docx skill
抽出：基本信息、教育、每段工作 / 项目经历的（公司、岗位、时间、职责 bullet）、技能列表。
Step 3: Gap 分析
调用：
python scripts/jd_gap.py --jd jd_parsed.json --resume resume.txt --out gap.md
脚本输出三类清单：
- 完美命中（JD must-have 在简历里有明确证据）
- 隐性命中（JD 要求 X，简历里有 X 的近义经验，但用词不一样 → 改写时可以"提一下"）
- 真缺口（JD 要求但简历完全没有）
对"真缺口"分两类：
- 可补救：简历里其实做过类似的事，只是没写出来 → 追问用户"你做过 X 吗？"
- 不可补救：用户确实没做过 → 不能编，建议用户在 cover letter 或 summary 里诚实说明并强调 transferable skill
Step 4: 定向改写
按以下原则重写简历：
a. 重排经历顺序：与 JD 最相关的工作 / 项目放最前（不改时间真实性，但可以把项目经历拆成两块"相关项目 / 其他项目"）
b. 重写每条 bullet：
- 把 JD 里的"职责动词"自然嵌入 bullet（如 JD 说"主导 ___ 系统设计"，简历里就把"参与"改成"主导"——前提是用户确实主导了）
- 数字保留并放大（"用户 100 万"是好事，别藏起来）
- 补 JD 关键词（如 JD 说"A/B 测试"，但简历里写的是"灰度对比"，改成"A/B 测试（灰度对比）"）
c. 重写 Summary：用 2~3 行总结你为什么是这个岗位的合适人选，直接对应 JD 的 must-have
d. 调整技能列表：把 JD 提到的技能移到最前面（前提是真的会）
e. 不改的事实：
- 公司名、岗位名、起止时间、学历 —— 一字不改
- 项目规模、用户量、收入数据 —— 不能编，只能让用户确认后填准
Step 5: 自检 + 报告
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 128 lines · 183 tokens per session scan A 635ddf0d5a1c
jd-resume-tailor is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 183 tokens to every session and 1,675 once invoked, about $0.0009 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 2026-09-03.
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