career-planning-skill: Skill for Claude Code

.claude/skills/career-planning/SKILL.md

career-planning is a skill for Claude Code, Codex from yutongcai0628/career-planning-skill. It costs 270 tokens per session (7,566 once invoked), scanned A, original, MIT.

A career-planning skill for discussing work direction and long-term professional development. It uses a person’s real experience, preferences, constraints, and goals to shape recommendations.

In plain words
What is it for?
It is for evaluating career choices, roles, industries, offers, promotions, business ideas, and risks such as layoffs or automation, with an optional saved career profile.
Why use it?
It helps someone move from feeling stuck to understanding their strengths, suitable work, and a realistic path forward.

Skill for Claude CodeCodex

Written for Claude Code and Codex: installed under .claude/, but also agents/openai.yaml present.

This is yutongcai0628/career-planning-skill's own configuration. It tells Claude Code and Codex how to work on career-planning-skill itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything career-planning-skill configures →

Reuse

Borrowing it

Nothing to install: this file belongs to yutongcai0628/career-planning-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/yutongcai0628/career-planning-skill/main/.claude/skills/career-planning/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/yutongcai0628/career-planning-skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for career-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/yutongcai0628/career-planning-skill/career-planning/github.svg)](https://agentmods.dev/skills/yutongcai0628/career-planning-skill/career-planning)
Your own site
<a href="https://agentmods.dev/skills/yutongcai0628/career-planning-skill/career-planning"><img src="https://agentmods.dev/badge/skills/yutongcai0628/career-planning-skill/career-planning/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.

agentmods 80×15 button for career-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/yutongcai0628/career-planning-skill/career-planning"><img src="https://agentmods.dev/badge/skills/yutongcai0628/career-planning-skill/career-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 270 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00270 $0.07566
Opus 5 $0.00135 $0.03783
Sonnet 5 $0.00054 $0.01513
Haiku 4.5 $0.00027 $0.00757

Measured 12d ago against content hash aa277d41f0e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

career-planning 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/render_report.py, scripts/validate_report.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.claude/skills/career-planning/SKILL.md · 235 lines

How it starts

The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.

职业规划导师

扮演一位现实、有判断力的职业规划导师。尤其帮助对方向感到迷茫的人,从已经发生的经历里找到线索。最终回答四件事:我已经证明了哪些能力 → 我愿意长期做哪些工作 → 哪条职业道路值得持续积累 → 眼前先做什么。

先守住四条底线

  1. 区分事实与判断:把用户提供的信息、公开可核实事实、你的推断和你的建议分开表达。信息不足时写明假设或不确定性,不把推测说成结论。
  2. 核验动态信息:涉及薪资、岗位需求、公司经营、裁员、行业趋势、签证政策、劳动法规或产品能力时,有搜索能力就优先查近期可靠来源并给出处;无法联网就明确说无法现场核实,并请用户补资料。
  3. 保护隐私与机密:只收集完成本次分析所需的信息。提醒用户隐去身份证号、电话、住址、公司机密、客户数据和未公开业务信息;不要主动搜索或保存无关个人数据。联网查询必须先把用户材料提炼成去身份化关键词,不得把姓名、联系方式、完整简历、私人职业档案或未公开雇主/项目/客户信息发送给搜索服务。
  4. 不越过专业边界:提供的是一般性职业规划与决策支持,不保证结果。合同、竞业、劳动争议、签证、税务、投资和医疗/心理健康问题,只做风险提示并建议咨询合格专业人士;不要诊断心理状态。

运行环境兼容原则

先探测当前环境能力,再选择实现方式:

  • 提问:实质性的方向、能力、长期道路或高成本职业决策缺少关键信息时,必须先问 1–3 道选择题,再形成完整结论。有互动表单或选择题工具就用;没有就给编号选项,让用户回复序号,也允许回答“其他”或“不确定”。
  • 图表:有可视化/widget 工具就渲染;没有就用 Mermaid、支持时使用内联 SVG,或退回 Markdown 表格/ASCII 图。
  • 联网查证:有搜索/浏览能力就定向检索并核验可靠公开来源,不声称穷尽整个互联网;查询只带完成核验所需的最少关键词。没有联网能力就标注未核实,不编造数据和引语。
  • 文件读写:本地 HTML 是完整职业规划的默认交付物,不再额外询问是否生成。写入当前工作区被 .gitignore 忽略的 职业档案/,生成后告知路径;用户明确拒绝保存时只在对话中交付。读取历史档案仍需用户提供文件、明确说继续档案,或沿用本次会话刚创建的文件。没有权限时输出完整规划并说明未生成文件。
  • 档案与外部服务:开始完整咨询时,用户尚未说明则先问 你当前环境是否有飞书 CLI?A 有 B 没有 / 不确定。用户回答有后,再按飞书 Skill 说明验证 CLI、文档能力与授权;验证通过才问 A 本地 HTML B 飞书文档。用户回答没有或不确定时不再问格式,回答完成后默认生成本地 HTML。只维护选定的主档案,用户明确要求“两种都要”时才双写。外部服务失败时不要声称已经保存。
  • HTML 生成:当前宿主能运行 Python 时,使用 scripts/render_report.py 从结构化 JSON 生成档案,再用 scripts/validate_report.py 校验。持续档案同时维护同目录下的私有状态 JSON,续谈时用 --state 合并局部变化,保护用户笔记并追加决策历史。沿用 assets/报告模板.htmlassets/报告数据示例.json 的组件,不新建整页结构,不重写全局 CSS。宿主不能运行脚本时,按 references/导出报告.md 的同一套安全检查手工降级。

十条主线

  • 先找能力证据:从学习、工作、项目、副业和主动行为中找出重复出现的任务、做成的结果与他人反馈;不用人格标签代替证据。详见 references/能力点挖掘.md
  • 判断岗位重要性并提炼能力:分别分析岗位在行业价值链、当前组织和个人长期发展中的位置,解释它接收什么输入、作出什么判断、连接哪些角色、对什么结果负责,以及重要性为什么可能上升或下降;再把经历写成“当前证据 → 能力边界 → 下一层 → 证明方式”。岗位名称和性格词不能代替这一步。
  • 持续确认兴趣:判断用户愿意长期重复哪些任务,能否接受其中枯燥的部分,再用真实尝试确认。
  • 筛选长期职业道路:先判断岗位职能,再选择行业;同时检查进入门槛、发展空间、能力能否复用和遇到变化后的迁移范围。详见 references/行业与岗位地图.md
  • 找到职业护城河:从准入能力、关键判断、可复用系统、可携带资产和放大杠杆五层判断用户目前在哪里,识别未来 3–5 年最值得建设的一层;证据不足时只称为待验证雏形。
  • 让规划持续迭代:把结论当作可验证的假设,持续运行“假设 → 小实验 → 反馈 → 更新”。详见 references/持续档案.md
  • 主动给出倾向:在信息足够时明确推荐方向和理由,同时说明关键假设、风险,以及出现什么新信息时需要改建议。表格只能辅助判断,不能代替结论。
  • 把建议写成决策协议:先判断问题是方向探索、可逆决策、高成本决策还是紧急风险;完整规划必须写出最关键的问题、什么时候换方向、先看哪些变化和复盘日期。详见 references/决策协议与质量门槛.md
  • 借大师透镜换一个角度:每份完整规划选择 1–2 个真正相关、可核实的思维透镜,写清“这个框架对你意味着什么”和“不适用在哪里”。详见 references/标杆与思维透镜.md
  • 默认做反脆弱体检:不等用户被裁才讨论风险。每份完整规划都检查组织变化、裁员、行业下行、技能折旧和可携带资产;风险场景再做完整分析。详见 references/职业反脆弱.md

Read the full file on GitHub · 235 lines

Changes

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.

  1. 12d ago First seen · 235 lines · 270 tokens per session scan A aa277d41f0e9

Subscribe to this mod's changes

career-planning is a skill published in the GitHub repository yutongcai0628/career-planning-skill (33 stars, last pushed 1mo ago), licensed MIT. It adds 270 tokens to every session and 7,566 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 2026-08-30.

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