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-gap-plannergit 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-gap-planner)<a href="https://agentmods.dev/skills/agentsope/career-skills/career-gap-planner"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-gap-planner/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-gap-planner"><img src="https://agentmods.dev/badge/skills/agentsope/career-skills/career-gap-planner.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 | $0.00288 | $0.03098 |
| Opus 5.5 | $0.00115 | $0.01239 |
| Sonnet 5.5 | $0.00058 | $0.00620 |
| Haiku 4.5 | $0.00029 | $0.00310 |
Grade A, and why
career-gap-planner 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Career Gap Planner · 能力差距规划器
把"我离这个岗还差什么、该怎么补"变成一张分清轻重、能落地、给真实资源的补强计划。这是 career-skills pack「定位投哪」环节里承上启下的一块——它接 career-jd-analyzer 拆出的岗位要求,对照你的现状,告诉你先补什么、怎么补、补到什么程度、要多久,以及哪些短期补不上、该怎么绕。
核心原则:诚实的差距,可行的路。 不灌鸡汤(不说"努力就能两周精通"),不画饼;短期补不上的如实讲并给替代策略;学习资源联网找真实、当前的,绝不编课程或链接。补强的终点是产出能写进简历的真实项目,形成闭环。
Activation Rules
触发(do):
- "我离这个岗 / 这个方向还差什么?"
- "我该学什么、怎么补?帮我做个计划。"
- "我想转去做 X(数据 / 产品 / 运营…),零基础该准备啥?"
- "JD 这些要求我不满足,怎么办?"
不触发(don't — 交给别的 skill):
- "这份 JD 到底要什么" →
career-jd-analyzer(先拆要求)。 - "有哪些岗位适合我 / 帮我找在招" →
career-role-finder。 - "帮我把补的技能写进简历" →
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 — 接目标 + 现状 (Intake · G1). 取得:(a) 目标岗位能力模型(优先用 career-jd-analyzer 的产出;没有则提示先拆 JD,或用户给目标);(b) 用户现状(已有技能 / 经历 / 学历,可来自 career-experience-mapper 或自述)。
→ 产出:目标能力清单 + 现状清单。
Step 2 — 诊断差距 (Diagnose · G2). Read references/gap-diagnosis.md。差距 = 目标 − 现状。给每个缺口标类型(硬门槛 / 核心技能 / 加分 / 软能力)和可补性(可补 / 难补 / 基本不可补)。
→ 产出:差距表(缺口 | 类型 | 可补性)。
Step 3 — 排优先级 (Prioritize · G3). 卡硬门槛且可补的优先,其次核心技能,再次加分项;不可补的单独标注(如统招学历)。砍掉性价比低的。 → 产出:排好序的"该补清单"。
Step 4 — 给路径 + 真实资源 (Path · G4). Read references/learning-paths.md 把每个该补缺口拆成"路径 + 可验证里程碑 + 现实时间";Read references/resource-finding.md,联网找真实、当前的学习资源给真实链接(B站 / 中国大学MOOC / Coursera / 官方文档 / GitHub…)。终点是产出一个能写进简历的真实项目。
→ 产出:每个缺口的可执行路径 + 真实资源 + 时间预期。
Step 5 — 现实 + 诚信闸门 (Reality & Integrity · G5). Read references/no-fabrication.md。核查:不可补的有没有如实说 + 给替代(换岗 / 绕开 / 长期)?有没有灌鸡汤 / 承诺不现实的时间?资源链接是不是真搜到的(没有就明说)?
→ 产出:通过 + 诚实提醒。
Step 6 — 输出 (Output). 结果优先:先一句"最该先补的是什么 + 大概多久能见效",再按需展开完整计划。
Core Operation Models
| # | 模型 Model | When to use | Key action |
|---|---|---|---|
| G1 | Intake 接目标+现状 | 开始 | 收 jd-analyzer 能力模型 + 用户现状 |
| G2 | Gap Diagnosis 差距诊断 | 对照 | 目标−现状;标类型 + 可补性 |
| G3 | Prioritize 排优先级 | 取舍 | 卡门槛且可补的优先;不可补单列 |
| G4 | Path & Resources 路径+资源 | 给方案 | 路径+里程碑+现实时间 + 联网找真实资源 |
| G5 | Reality & Integrity Gate(红线) | 输出前 | 不可补如实说;不灌鸡汤;不编资源链接 |
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 First seen · 162 lines · 288 tokens per session scan A 4d62a68d7a0a
career-gap-planner is a skill published in the GitHub repository agentsope/career-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 288 tokens to every session and 3,098 once invoked, about $0.0012 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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