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-experience-mappergit 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-experience-mapper)<a href="https://agentmods.dev/skills/agentsope/career-skills/career-experience-mapper"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-experience-mapper/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-experience-mapper"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-experience-mapper.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 | $0.00239 | $0.03864 |
| Opus 5 | $0.00120 | $0.01932 |
| Sonnet 5 | $0.00048 | $0.00773 |
| Haiku 4.5 | $0.00024 | $0.00386 |
Grade A, and why
career-experience-mapper 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Career Experience Mapper · 经历→岗位语言 编译器
把"我做过一个小组作业""帮老师整理过数据""跟教程复现了一个模型"这种原始经历，编译成招聘方读得懂、且完全属实的岗位语言。这是 career-skills pack 的第一块、也是灵魂——它的差异化不在"会写简历"，而在 弱经历→岗位语言 + 绝不编造。
核心原则 / Core principle：Reframe, never fabricate. 改变表达视角与精度，绝不改变事实。证据不足就用保守动词或标注待补充，绝不替用户编数字、头衔、成果或链接。
Activation Rules
触发（do）：
- 用户给出一段或多段经历，问"这能写进简历吗 / 怎么写 / 体现了什么能力"。
- "帮我把这段经历变成简历表达 / bullet 思路"。
- "我没有实习经历 / 这段经历太水 / 都是课程作业，怎么办"。
- 转专业、留学生、应届生问"我的经历怎么和这个岗位对上"。
- 想把中文经历转成英文 CV 表达(英文按需)。
不触发（don't — 交给别的 skill）：
- 已有成稿 bullet 只想润色字句 →
career-bullet-builder。 - "有哪些岗位适合我 / 帮我找在招岗位" →
career-role-finder。 - "帮我拆解这份 JD 的能力要求" →
career-jd-analyzer。 - 要 cover letter、要面试题 / 面试故事 → 对应的 interview-* skill。
- 与求职无关的纯语言润色。
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，不要把整份 reference 贴给用户。
Step 1 — 收集输入 (Intake). 取得：(a) 原始经历（中/英、口语均可，可多段）；(b) 目标岗位或 JD（可选）。 没有 JD 时进入"通用萃取"模式，并提示用户："给我目标岗位或 JD，能让能力对齐更准。" → 产出：归一化的经历清单 + 目标岗位（或"通用"）。
Step 2 — 解析经历 (Parse · M1). 对每段经历抽取六要素：任务 / 动作 / 方法·工具 / 协作对象(stakeholders) / 产出 / 可量化线索。口语和模糊处先如实标记，不脑补。 → 产出：每段经历的要素表。
Step 3 — 萃取可迁移能力 (Map · M2). Read references/transferable-skills.md。把要素映射到 NACE 8 能力（必要时补 O*NET 子技能）。对每个能力过证据闸门：能不能讲出一个 2 分钟 STAR 故事？讲不出 → 从"已具备"降为"发展中"或删。Leadership 默认从严：没有"定方向 / 解决冲突"的具体故事，就降级为 Teamwork。
→ 产出：可迁移能力清单（能力 → 证据 → 强/弱标记）。
Step 4 — 对齐岗位 (Align · M4). 有 JD：Read references/role-matching.md，对每段经历用 2/4 标准做相关性分级（强 Foreground / 弱 Reframe / 无关则 Downplay 或 Cut），并从 JD 按"频率×位置"提关键词。无 JD：做通用萃取，并标注哪些能力一旦给定岗位可前置。
→ 产出：每段经历的相关性判定 + 关键词覆盖情况。
Step 5 — 翻译成岗位语言 (Translate · M5). Read references/role-language-bank.md（必要时 weak-exp-transformations.md 取同类范例）。选公式（默认 PAR；有真实数字用 XYZ；技术项目用 CAR），把弱动词升级为精确强动词，产出中文岗位语言草表达（去口语 / 谦辞，不加原文没有的事实）。没有数字时走量化 fallback（技术岗优先技术细节），绝不编数字。用户要英文版再做本地化（去 Chinglish，同样不加事实）。
→ 产出：每段经历的中文草表达（可直接交给 career-bullet-builder 打磨）。
Step 6 — 诚信校验 (Integrity Gate · M3). Read references/no-fabrication.md，逐条过自检：有没有编造雇主/职称/日期/数字/奖项/技能/URL？团队成果是否标了个人范围？动词层级是否匹配真实贡献？翻译有没有偷偷加事实？相关性有没有硬吹？任何拿不准的事实（具体数字、头衔范围、是否独立完成）直接问用户，不臆造。
→ 产出：通过 / 标记项 + 待补充信息清单。
What ships with it
8 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 · 180 lines · 239 tokens per session scan A 6fe07b491c26
career-experience-mapper is a skill published in the GitHub repository agentsope/career-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 239 tokens to every session and 3,864 once invoked, about $0.0012 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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