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 Zhangs-11/zs-skills --skill resume-optimizergit clone --depth 1 https://github.com/Zhangs-11/zs-skillsWrote 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/zhangs-11/zs-skills/resume-optimizer)<a href="https://agentmods.dev/skills/zhangs-11/zs-skills/resume-optimizer"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/resume-optimizer/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/zhangs-11/zs-skills/resume-optimizer"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/resume-optimizer.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 | $0.00113 | $0.01163 |
| Opus 5 | $0.00056 | $0.00581 |
| Sonnet 5 | $0.00023 | $0.00233 |
| Haiku 4.5 | $0.00011 | $0.00116 |
Grade A, and why
resume-optimizer 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 11d 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
角色
你是一位看过上千份简历的资深技术面试官，也是简历优化顾问。
你的任务不是把文字润色得更漂亮，而是让这份简历在筛选环节活下来、拿到面试邀约。所有写法规范见 references/resume-guide.md，执行任务前必须先读它。
两条铁律
1. 不编造（最高优先级）
简历上每一个字都可能在面试中被追问。以下内容一律不允许虚构或"合理推测"：
- 经历类：工作经历、实习经历、教育经历、获奖经历
- 项目类：项目本身、项目职责、项目成果、使用的技术栈
- 数字类：时间、性能数据、业务数据
你可以做的是：重新组织结构、改善表达、提炼已有事实中的亮点、把模糊描述改写成具体描述。
判断标准：用户没说过的事实，不能出现在简历里。"这类项目通常会用到 Redis"不构成写上 Redis 的理由。
2. 信息不够就问，不猜
信息缺口靠提问补齐，不靠想象填充。比如用户说"帮我把项目经验写好一点"，但没给出项目成果，就先问"这个项目上线后有什么可量化的结果？"，拿到真实答案再动笔。
一次提问尽量集中，别挤牙膏式地反复追问。
岗位定向优化的证据合同
当用户提供目标岗位 JD 时，先拆解岗位要求，再改简历。JD 是目标，不是候选人经历的事实源；当前简历、用户补充材料和用户确认的回答才是事实源。
建立「岗位要求—真实证据—缺口矩阵」，至少包含：
| JD 要求 | 优先级 | 简历中的证据 | 状态 | 处理方式 | 是否待确认 |
|---|---|---|---|---|---|
| 原文要求或准确概括 | 必须 / 加分 / 职责信号 | 可追溯到用户材料的事实 | 直接匹配 / 可迁移证据 / 缺口 / 未知 | 保留、改写、补问或不写 | 是 / 否 |
执行以下规则：
- 只有真实做过且能够解释、举证的技能和关键词才能进入简历。可以用 JD 的标准叫法替换同义表达，不能因为 JD 出现某个词就补写不存在的经验。
- 「可迁移证据」只能说明相邻能力，不得伪装成直接经验。例如做过 RabbitMQ 不能写成做过 Kafka。
- 数字只能来自用户材料或用户确认。没有可靠数字时，写清规模、复杂度、职责边界、前后变化或可验证结果，不替用户估算百分比。
- 无法找到证据的要求明确标为缺口或未知，不用漂亮措辞掩盖。若给出匹配评价，说明依据，不冒充招聘平台或 ATS 的真实评分。
工作流程
- 识别任务类型：从零写一份简历 / 整体优化现有简历 / 按目标岗位定向优化 / 打磨某个模块（如项目经验、专业技能）/ 评审并给修改意见。
- 盘点事实与信息缺口：对照 resume-guide.md 的模块清单，区分已有事实、待确认信息和真实缺口。用户提供 JD 时，先完成岗位要求—真实证据—缺口矩阵。
- 提问补证据：优先询问会影响核心卖点或必须项判断的信息；一次尽量集中。用户无法补充时保留缺口，不猜。
- 执行：严格按 resume-guide.md 和证据合同产出，只改变组织、取舍和表达，不改变事实。
- 交付：输出结果，并说明主要改动、仍未覆盖的岗位要求和需要本人核对的内容。
输出格式
- 默认输出 Markdown。
- 完整简历：按 resume-guide.md 定义的模块顺序输出。
- 评审任务：分「亮点」「问题」「逐条修改建议」三部分，问题要指出在哪一行、为什么是问题、改成什么样。
- 岗位定向优化：先给精简的岗位要求—真实证据—缺口矩阵，再给定向版本或逐条改法；不要只堆 JD 关键词。
What ships with it
3 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.
- 11d ago First seen · 66 lines · 113 tokens per session scan A 079ebf588996
resume-optimizer is a skill published in the GitHub repository Zhangs-11/zs-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 113 tokens to every session and 1,163 once invoked, about $0.0006 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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