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 agentsope/career-skills --skill career-resume-tailorgit clone --depth 1 https://github.com/agentsope/career-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/agentsope/career-skills/career-resume-tailor)<a href="https://agentmods.dev/skills/agentsope/career-skills/career-resume-tailor"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-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/agentsope/career-skills/career-resume-tailor"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-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 | $0.00300 | $0.03084 |
| Opus 5 | $0.00150 | $0.01542 |
| Sonnet 5 | $0.00060 | $0.00617 |
| Haiku 4.5 | $0.00030 | $0.00308 |
Grade A, and why
career-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 12d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Career Resume Tailor · 简历投递定制器
把一堆打磨好的 bullet 和"我要投这个岗",变成一份结构清楚、详略得当、对得上岗位、可直接投递的中文简历。这是 career-skills pack「做简历」环节的收口——上游 career-bullet-builder 把每条经历磨成成品行,本 skill 决定这份简历放哪些、怎么排、砍到多长、怎么对上这个岗。
核心原则:Tailor to the job, never fake the job. 针对岗位做取舍和侧重,但不编造经历、不编造数字、不编造职位链接。真实 JD 由用户提供或联网抓取;抓不到就明说,绝不造 URL。
Activation Rules
触发(do):
- "帮我把简历针对这个岗位调一下 / 投这个岗要改啥?"
- "把这些经历组装成一份简历。"
- "我简历太长 / 太乱,帮我砍到一页、排好。"
- "同一份简历投运营和投数据,怎么各出一版?"
- "这份简历投 X 岗合适吗?投递前帮我体检。"
不触发(don't — 交给别的 skill):
- "帮我把这一条写得更有力" →
career-bullet-builder(单条打磨)。 - "我这段经历能体现什么能力" →
career-experience-mapper。 - "有哪些岗位适合我 / 帮我找在招" →
career-role-finder。 - "帮我深扒这份 JD 的能力要求" →
career-jd-analyzer。
Agentic Protocol
按顺序执行;每步有可验证产出。涉及方法细节时按需 Read 对应 reference。
Step 1 — 接素材 (Intake · T1). 取得:(a) career-bullet-builder 的成品 bullets(或用户已有简历);(b) 目标岗位 / JD——优先要真实链接(牛客 / Boss / 实习僧等),用户给链接我联网抓,或贴 JD 文本。没有目标岗位时提示:"给我目标岗位或 JD 链接,我才能对着调;否则只能出通用版。"
→ 产出:bullet 池 + 目标 JD(真实)或"通用"。
Step 2 — 取舍与排序 (Select & Order · T2). 按与 JD 的相关性给经历/bullet 分级:强相关前置、弱相关精简、无关删。最相关的经历放最前、每段最有力的 bullet 放第一条。 → 产出:留用清单 + 排序。
Step 3 — 结构编排 (Structure · T3). Read references/resume-structure.md。定模块顺序(应届默认:教育 → 实习/项目 → 技能 → 校园经历;有强相关实习则前移)、倒序排列、砍到合适长度(应届一页)。
→ 产出:整份简历骨架。
Step 4 — 针对岗位定制 (Tailor · T4). Read references/tailoring-by-jd.md。整体对齐 JD 关键词(用原词)、按岗位调整详略侧重;要投多个岗位时出多个投递版本。需要真实岗位/JD 时 Read references/job-platforms.md 取真实来源。
→ 产出:针对该岗位的简历(可多版本)。
Step 5 — 诚信 + 格式 + 合规闸门 (Gate · T5). Read references/no-fabrication.md。核查:没有为对岗位而编造经历/数字/链接;继承的 gaps 未确认数字不写死;ATS 格式无雷(见 resume-structure.md);个人信息合规(照片/出生日期/性别等不进简历)。
→ 产出:通过 / 待补充 + 投递建议。
Step 6 — 输出 (Output). 按结果优先给整份简历 + 一句最关键的补强/取舍提示 + 一行"想要更多"。
Core Operation Models
| # | 模型 Model | When to use | Key action |
|---|---|---|---|
| T1 | Intake 接素材 | 拿到 bullets + 目标岗位 | 收成品 bullet + 真实 JD(链接优先,联网抓) |
| T2 | Select & Order 取舍排序 | 决定放哪些、怎么排 | 按 JD 相关性 前置/精简/删 + 影响力排序 |
| T3 | Structure 结构编排 | 组装整份 | 模块顺序 + 倒序 + 砍到一页 |
| T4 | Tailor 针对岗位 | 对上特定岗位 | 关键词对齐 + 详略侧重 + 多投递版本 |
| T5 | Integrity+Format Gate(红线) | 输出前必过 | 不编经历/数字/链接;ATS 格式;信息合规 |
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.
- 12d ago First seen · 165 lines · 300 tokens per session scan A 705766820fde
career-resume-tailor is a skill published in the GitHub repository agentsope/career-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 300 tokens to every session and 3,084 once invoked, about $0.0015 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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