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-role-findergit 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-role-finder)<a href="https://agentmods.dev/skills/agentsope/career-skills/career-role-finder"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-role-finder/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-role-finder"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-role-finder.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 | $0.00286 | $0.03118 |
| Opus 5 | $0.00143 | $0.01559 |
| Sonnet 5 | $0.00057 | $0.00624 |
| Haiku 4.5 | $0.00029 | $0.00312 |
Grade A, and why
career-role-finder 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Career Role Finder · 岗位定位与在招检索
回答求职第一个问题:"我到底该投什么?" 基于你的背景给出够得着、对得上的岗位方向,并联网找出真实在招的具体职位(给真实链接)。这是 career-skills pack 的起点——定位准了,后面拆 JD、补差距、写简历才有的放矢。
核心原则:Real openings only, never invent. 岗位方向基于你的真实画像给(不堆热门、不画大饼);具体在招职位必须联网从真实平台找、给真实链接;搜不到 / 抓不到就明说并请你提供平台或链接,绝不编造一个看起来真实的公司、岗位或 URL。职位会下架——所有链接标注抓取时间,投递前请自行确认。
Activation Rules
触发(do):
- "我这个背景能投什么岗 / 什么方向?"
- "有没有适合我的实习 / 校招岗位?帮我找在招的。"
- "我不知道自己适合做什么。"
- "帮我在 牛客 / Boss / 实习僧 上找 XX 岗。"
不触发(don't — 交给别的 skill):
- "这份 JD 到底要什么" →
career-jd-analyzer。 - "我离这个岗还差什么 / 该学啥" →
career-gap-planner。 - "帮我写 / 改简历投这个岗" →
career-bullet-builder/career-resume-tailor。 - "我这段经历体现什么能力" →
career-experience-mapper。
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 — 收画像 (Profile · F1). 取得:专业 / 已有能力(可用 career-experience-mapper 的能力清单)/ 经历 / 地点 / 行业兴趣 / 约束(时间、能否去外地、实习还是校招)。信息不足直接问关键几项,不脑补。
→ 产出:用户求职画像。
Step 2 — 定方向 (Direction · F2). Read references/role-targeting.md。基于画像给 2-3 个岗位方向:主攻 + 相邻 / 退路;每个标"为什么适合你"和"现实可达性"。不只推热门、不画够不着的饼。
→ 产出:岗位方向清单 + 适配理由。
Step 3 — 联网找在招 (Live Search · F3). Read references/live-search.md 与 references/job-platforms.md。用 WebSearch / WebFetch 在真实平台(牛客 / Boss / 实习僧 / 官网校招 / 学校就业网)搜当前在招、匹配方向的具体职位。
→ 产出:真实职位列表(公司 / 岗位 / 真实链接 / 抓取时间)。
Step 4 — 筛选呈现 (Screen · F4). 按匹配度 + 可达性筛掉明显不合的;每个保留项给"为什么推给你"。区分"够得着"和"冲一冲"。 → 产出:精选职位(带链接 + 理由)。
Step 5 — 诚信闸门 (Integrity Gate · F5). Read references/no-fabrication.md。核查:列出的每个职位 / 公司 / 链接都是真实搜到的?有没有为"凑数"编造?搜不到的有没有如实说?链接有没有标抓取时间 + 提示自行确认?匹配理由有没有夸大?
→ 产出:通过 + 真实性标注。
Step 6 — 输出 (Output). 结果优先:先给"建议主攻方向 + 几个真实在招",再按需展开;并指出下一步(拆某个 JD → jd-analyzer;不满足 → gap-planner)。
Core Operation Models
| # | 模型 Model | When to use | Key action |
|---|---|---|---|
| F1 | Profile 收画像 | 开始 | 收 专业/能力/经历/地点/偏好/约束;缺就问 |
| F2 | Direction 定方向 | 给方向 | 2-3 个方向(主攻+相邻+退路)+ 现实可达性 |
| F3 | Live Search 联网找在招 | 找具体岗 | WebSearch/WebFetch 真实平台,当前在招 |
| F4 | Screen 筛选呈现 | 给清单 | 按匹配+可达性筛;每个给推荐理由 |
| F5 | Integrity Gate(红线) | 输出前 | 岗位/链接全真实;搜不到就说;标时间;不夸大 |
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 · 164 lines · 286 tokens per session scan A f709c57322f7
career-role-finder is a skill published in the GitHub repository agentsope/career-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 286 tokens to every session and 3,118 once invoked, about $0.0014 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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