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 asherzj/ashers-agent-skills --skill write-resumegit clone --depth 1 https://github.com/asherzj/ashers-agent-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/asherzj/ashers-agent-skills/write-resume)<a href="https://agentmods.dev/skills/asherzj/ashers-agent-skills/write-resume"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/write-resume/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/asherzj/ashers-agent-skills/write-resume"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/write-resume.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 | $0.00082 | $0.04821 |
| Opus 5.5 | $0.00033 | $0.01928 |
| Sonnet 5.5 | $0.00016 | $0.00964 |
| Haiku 4.5 | $0.00008 | $0.00482 |
Grade A, and why
write-resume 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 1mo 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
简历写作
目标
把原始经历转化为简洁、可信、能赢得面试机会的简历。把简历当作一份“决策材料”:每个部分都应该帮助读者判断“这个人值得进入面试”。
核心原则
- 提供决策点:用事实支撑候选人值得继续推进。
- 只在真实且可自证的前提下制造挑战点:强表达应该能引出面试问题,并且候选人答得出来。
- 内容组织优先呈现最强证据,不机械复制模板中的示例内容;生成正式文件时,视觉和布局仍必须遵循“模板一致性”要求。
- 用证据替代标签。不要只说“owner 意识强”,要通过范围、决策、取舍和结果展示 ownership。
- 量化有决策价值的结果;没有可靠数字时,可以写清技术取舍、交付范围或团队工作方式的变化,不为了每条都有数字而添加弱指标。
- 数值变化优先写同口径的“从 A 到 B”,并核对统计周期、样本范围和比较对象。只有终值时可客观陈述终值;只有有据可查的变化比例时保留比例及口径,不倒推未知基线。无法支持的变化结论改为已确认事实,关键缺口才追问。百分比与百分点必须区分,例如从 18% 到 24% 是提升 6 个百分点。
- 区分个人贡献、系统规模与业务共同结果。读取双跑不等于双写,协作人数不等于直属团队,迁移零事故不等于整个平台零故障。日常与应急、规则发布与规划发布等指标不合并;不同版本口径不同,不能直接判断哪个结果更好。
- 保持真实。可以优化表达,但不要编造事实、数字、职级或 ownership。资料不足时,使用待确认占位符、提出针对性问题,或说明可补充的证据类型。
- 面向快速浏览写作:每条只表达一个重点,强信息前置。每个 bullet 尽量一行,一行是默认目标,两行是有理由的例外。 先删除重复背景、空泛修饰和次要实现细节,再收紧句式;只有继续压缩会丢失关键取舍、个人贡献、结果或必要指标范围时,才保留接近完整的两行。不同贡献可以拆条,但不把同一行动与结果机械拆开。不得缩小字号、压缩页边距、强制不换行或删去必要口径来凑一行。
- 跨行重要文本块的末行不得只剩少量字词。检查范围至少包括 bullet、项目描述、角色说明、联系方式、目标岗位、项目标题和 Mock 标识;末行必须同时包含至少 8 个有效字符并占该文本块参考行宽至少 15%。固定流水线以 PDF 实际排版坐标强制检查,任一不满足即构建失败。
- 非末页不得因显式分页、整体防拆或不合理的内容编排留下大块底部空白。固定流水线要求非末页最下方可见内容默认达到页面高度的 82%;未达到时先修复分页和内容流,不得靠缩小字体、压缩页边距或降低门禁掩盖问题。
- 让简历 bullet 能匹配面试故事。简历给出论点和关键证据,面试展开细节。
工作流程
-
确定任务与交付形式
- 先判断用户需要比较/诊断、整体改写、局部修改、针对 JD 定制,还是生成完整文件。比较或诊断只读原件,先给判断和证据,不自动改写或导出。
- 比较版本时读取用户指定的实际附件,不用同名旧文件或可编辑稿代替;PDF 同时检查正文和所有相关页面的实际渲染。区分表达质量与事实核验,不凭排版或更大数字判优。
- 局部修改继承最新已接受稿和偏好,只处理指定内容;不擅自恢复旧章节顺序、迁移术语或指标范围。
- 用户指定的格式、模板和交付方式优先;修改现有简历时,默认保留原格式和有用的视觉设计。生成新的完整简历文件时,必须执行下方“模板一致性”规则。
- 用户只需要内容优化时,直接在对话中交付,不额外生成文件。
-
引导用户提供信息
- 生成完整简历或进行大范围重写前,读取
references/resume-intake.md,根据其中的采集表引导用户提供目标岗位、原始经历、证据、教育与技能、输出要求和隐私约束。 - 先从用户已经提供的简历、JD、作品集、经历笔记和对话中提取信息,列出“已掌握的信息”和“仍影响成稿的关键缺口”;不要让用户重复填写已有内容。
- 用户没有现成简历时,给出可直接复制填写的采集表。明确说明无需润色,可以提供零散事实;不知道、不适用或不愿披露的项目可分别标记为“待补充”“不适用”或“不披露”。
- 局部诊断或改写只收集完成当前任务所需的信息,不要求用户填写完整采集表。
- 优先一次性追问会改变岗位定位、内容取舍或强主张可信度的缺口。非关键缺口可以保留待确认占位符,不要以信息不完整为由阻塞可交付的草稿。
- 生成完整简历或进行大范围重写前,读取
-
明确目标
- 明确目标岗位、职级、行业、公司类型,以及简历主要面向真人阅读、ATS 系统,还是两者兼顾。
- 如果有 JD,提取核心要求和必要关键词。
- 如果目标未知,基于用户最强证据优化到最合理的目标岗位。
-
盘点证据
- 收集工作经历、教育背景、项目、成果、指标、奖项、论文、开源项目、作品集和有意义的兴趣爱好。
- 核对数值变化的基线、终值或已证实比例,以及统计周期和样本范围;只追问影响定位或强主张的缺口,其他内容使用已确认事实继续成稿。
- 区分“项目发生了什么”和“候选人个人负责了什么”。
- 区分原文记录、用户本轮确认、待核实与未来计划;用户确认可以作为来源,但不等于外部核验。生成或实质改写时,在用户材料目录维护简短来源说明:
最终表述 | 来源/本轮确认 | 范围与待核实项;只读评价无需新建记录。
What ships with it
7 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.
- 1mo ago Changed · +6 lines · -6 tokens per session 3398c820f56c
- 1mo ago First seen · 157 lines · 88 tokens per session scan A 3cd139ab5a4f
write-resume is a skill published in the GitHub repository asherzj/ashers-agent-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 4,821 once invoked, about $0.0003 per session on Opus 5.5. 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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