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AI时代职业规划助手

by bettermen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install ai-career-planner
Description
AI时代职业规划助手。基于用户当前职业画像,评估AI自动化风险,分析技能差距, 推荐AI时代新职业方向,生成包含12个月转型行动计划的交互式HTML可视化报告。 覆盖技术/产品/运营/设计/市场/行政等核心岗位类别。 Triggers: 职业规划, AI时代职业, 职业转型, 未来职业, AI替代风险, 职业方向...
README (SKILL.md)

AI时代职业规划助手 (AI Career Planner)

AI-powered career planning assistant for the AI era. Evaluate automation risk, analyze skill gaps against AI-era competencies, recommend new career paths, and generate a personalized 12-month transition roadmap as an interactive HTML report.

When to Use

Trigger this skill when the user:

  • Asks for career planning advice in the AI era
  • Wants to know if their job will be replaced by AI
  • Seeks career transition or upskilling advice
  • Uses keywords: 职业规划, AI时代, 职业转型, AI替代, 职业方向, 转行, 技能提升, 职业生涯, 一人公司, 超级个体, career planning, AI career, job risk

Skill Resources

  • references/ai_jobs_catalog.md — 2026 AI-era new job catalog with 20+ emerging roles
  • references/risk_factors.md — Automation risk assessment framework & scoring methodology
  • references/competency_framework.md — AI-era core competency model (5 dimensions)
  • assets/report_template.html — Interactive HTML report template with {{PLACEHOLDERS}}

Core Workflow

Phase 1: Career Profile Collection

Collect the following in a structured, conversational way. Ask in batches to avoid overwhelming the user.

Batch 1: Basic Info (REQUIRED)

【第一步:基本信息】

1. 你当前的职业/岗位名称是什么?
2. 所在行业?(如:互联网/金融/教育/制造/医疗/政府/零售...)
3. 工作年限?
4. 最高学历和专业背景?

Batch 2: Current Role Details

【第二步:岗位详情】

5. 你每天的主要工作内容是什么?(描述3-5项核心任务)
6. 工作中使用AI工具的频率?
   A. 每天使用多个AI工具
   B. 偶尔使用1-2个
   C. 听说过但没用过
   D. 完全不了解
7. 你工作中最核心的3项技能是什么?

Batch 3: Career Goals

【第三步:职业目标】

8. 你对当前职业发展最大的担忧是什么?
9. 你期望的转型方向?(可多选)
   A. 在原岗位升级AI技能
   B. 转行AI相关新岗位
   C. 成为自由职业者/超级个体/一人公司
   D. 不确定,需要建议
10. 期望的转型时间窗口?
   A. 3个月内  B. 6-12个月  C. 1-2年  D. 不着急

Batch 4: Additional Context (Optional)

【第四步:补充信息(可选)】

11. 你所在城市?(影响就业机会和薪资判断)
12. 当前薪资范围?(用于评估转型成本)
13. 是否有管理经验?团队规模?
14. 你最有成就感的项目或经历?

Rule: At minimum, collect Q1-Q8 before proceeding to Phase 2. Mark any unanswered optional questions as "未提供".


Phase 2: AI Automation Risk Assessment

2.1 Load Risk Framework

Read references/risk_factors.md to load the complete risk assessment framework.

2.2 Score Calculation

Calculate the AI Automation Risk Score (0-100) based on:

Risk Factor Weight Scoring Logic
任务重复性 25% High repetition → high risk. From user's core tasks (Q5).
创造力需求 20% Low creativity → high risk. Inversely proportional.
人际交互深度 15% Low human interaction → high risk.
非结构化决策 20% Rule-based decisions → high risk.
职业技能可数字化程度 10% Fully digital → high risk.
AI工具使用熟练度 10% From Q6. No AI usage → higher risk.

Score thresholds:

  • 0-30: 低风险 — AI目前难以替代,但建议持续升级
  • 31-55: 中等风险 — 部分工作可被自动化,需要战略调整
  • 56-75: 较高风险 — 核心工作面临自动化,建议6-12个月内转型
  • 76-100: 高风险 — 岗位处于AI替代前沿,建议立即启动转型

2.3 Task-Level Breakdown

For each core task the user listed (Q5), classify:

  • 🔴 高替代风险:规则明确、重复度高、输入输出结构化
  • 🟡 中等风险:部分需要判断、有一定创造性
  • 🟢 低风险:需要深度创造力、情感互动、复杂决策

Phase 3: AI-Era Competency Gap Analysis

3.1 Load Competency Framework

Read references/competency_framework.md for the 5-dimension model.

3.2 Five Core AI-Era Competencies

# Competency Description Weight
1 AI思维与人机协同 驾驭AI工具进行决策和创作 25%
2 跨学科整合能力 融合多领域知识,定义复杂问题 20%
3 审美与判断力 AI内容甄别、创意定向、质量把控 20%
4 原始创新力 从0到1定义新问题和新方案 20%
5 情感与领导力 团队协作、共情沟通、影响力 15%

3.3 Gap Scoring

For each competency, rate the user on a 1-5 scale (based on their self-reported info):

  • 1: 完全缺失 — 核心短板,需优先补足
  • 2: 基础薄弱 — 需要系统学习
  • 3: 基本具备 — 需要深度强化
  • 4: 较强 — 需保持并发挥优势
  • 5: 专家级 — 核心竞争力,继续深耕

Calculate the AI-Ready Index = weighted average × 20 (scale to 0-100).

Index thresholds:

  • 0-40: 急需提升 — AI时代竞争力严重不足
  • 41-60: 有基础但薄弱 — 需要系统性的能力构建
  • 61-80: 具备AI时代基本能力 — 继续强化优势维度
  • 81-100: AI-Ready — 已具备AI时代的核心竞争力

Phase 4: Career Path Recommendations

4.1 Load Job Catalog

Read references/ai_jobs_catalog.md for the full AI-era job catalog.

4.2 Recommendation Engine

Generate recommendations in 3 tiers:

Tier 1: 顺势升级 (Upgrade in Place) — Stay in current role but integrate AI

  • Add AI tools to existing workflow
  • Pursue certification in AI-related field
  • Take on AI-related projects in current company

Tier 2: 相近转型 (Adjacent Transition) — Move to AI-adjacent role in same industry

  • Map user's domain expertise to emerging AI roles
  • Select from ai_jobs_catalog.md based on industry match
  • Recommend 2-3 specific roles with transition difficulty rating

Tier 3: 全新赛道 (New Track) — Radical career change

  • For high-risk users: explore entirely new AI-era career paths
  • Consider "超级个体/一人公司" pathway
  • Include entrepreneurship/freelance options

4.3 Recommendation Scoring

For each recommended role, provide:

  • 匹配度 (Fit Score): 1-10 based on skill transferability
  • 转型难度 (Difficulty): Easy / Medium / Hard
  • 学习周期 (Learning Curve): 3个月 / 6个月 / 12个月 / 18个月+
  • 薪资前景 (Salary Outlook): ↓ / → / ↑ / ↑↑
  • AI稳定性 (AI Resilience): Low / Medium / High

Phase 5: Action Plan Generation

5.1 12-Month Transition Roadmap

Generate a 4-quarter plan:

Quarter Focus Key Actions
Q1 (1-3月) 认知升级 & 基础构建 AI工具熟练、行业趋势学习、能力自评
Q2 (4-6月) 技能深化 & 实践积累 系统学习核心技能、参与AI项目、建立作品集
Q3 (7-9月) 网络构建 & 市场验证 行业交流、面试尝试、个人品牌建设
Q4 (10-12月) 转型落地 & 持续迭代 岗位转换/自由职业启动、持续学习体系搭建

5.2 Learning Resources

Recommend 3-5 specific learning resources based on user's target direction:

  • Online courses (Coursera, 学堂在线, 网易云课堂)
  • Books
  • Communities & networks
  • Tools to master

5.3 Risk Mitigation Tips

  • 不要把鸡蛋放在一个篮子里:发展Plan B
  • 建立"斜杠"能力组合
  • 保持与行业前沿的连接
  • 定期(每季度)重新评估职业方向

Phase 6: HTML Report Generation

6.1 Prepare Data

Compile all analysis results:

  • User profile summary
  • Risk scores (overall + per task)
  • Competency gap radar data
  • Career recommendations (3 tiers)
  • 12-month action plan
  • Learning resources

6.2 Generate HTML Report

  1. Read assets/report_template.html as the base template
  2. Replace all {{PLACEHOLDERS}} with computed data:
Placeholder Source Description
{{USER_NAME}} Derived or "职场人" User identification
{{CURRENT_ROLE}} Q1 Current job title
{{INDUSTRY}} Q2 Industry
{{YEARS_EXP}} Q3 Years of experience
{{RISK_SCORE}} Phase 2 calculation 0-100 risk score
{{RISK_LEVEL}} Risk score threshold 低/中等/较高/高风险
{{RISK_LEVEL_CLASS}} CSS class low/medium/high/critical
{{RISK_BREAKDOWN}} Generated HTML Per-factor risk breakdown
{{TASK_ANALYSIS}} Generated HTML Task-level risk table
{{AI_READY_INDEX}} Phase 3 calculation 0-100 index
{{COMPETENCY_RADAR_DATA}} Phase 3 scores JavaScript radar chart data
{{COMPETENCY_BARS}} Generated HTML 5-dimension bar visualization
{{TIER1_RECOMMENDATIONS}} Generated HTML Upgrade-in-place recommendations
{{TIER2_RECOMMENDATIONS}} Generated HTML Adjacent transition roles
{{TIER3_RECOMMENDATIONS}} Generated HTML New track options
{{QUARTERLY_PLAN}} Generated HTML 4-quarter action plan
{{LEARNING_RESOURCES}} Generated HTML Recommended resources
{{REPORT_DATE}} Current date YYYY-MM-DD
{{SCORE_CHART_JS}} Generated JS Gauge chart initialization

6.3 Visual Elements

The report includes:

  • Risk Gauge: Semi-circular gauge showing AI automation risk (0-100)
  • Task Analysis Table: Color-coded task risk assessment
  • Competency Radar Chart: 5-axis radar for AI-era competencies
  • Competency Bars: Horizontal progress bars with scores
  • Career Recommendations: Three-tier card layout with role cards
  • Quarterly Timeline: 4-column grid for 12-month plan
  • Resources Section: Curated learning materials

6.4 Write and Deliver

  1. Write the completed HTML to ai-career-plan-{{TIMESTAMP}}.html in workspace
  2. Present with preview_url
  3. Deliver with deliver_attachments
  4. Provide a text summary:
    • Risk score + level
    • AI-Ready Index
    • Top 3 recommended career moves
    • First action to take this week

Interactive Mode Details

Question Flow Control

  • Ask in batches of 3-5 questions at a time
  • Confirm answers before proceeding to next batch
  • Allow the user to skip optional questions (Q11-Q14)
  • If the user gives vague answers, ask for clarification ("能具体描述一下你的日常工作任务吗?")
  • If the user seems unsure about career goals (Q9 selects D), spend extra time in Phase 4 exploring options

Handling Edge Cases

  • Student/New Grad: Adapt risk assessment — focus on "first career choice" rather than "transition"
  • Career Changer: Weight Phase 5 action plan more heavily
  • Senior Executive: Emphasize leadership + AI strategy over individual tool skills
  • Freelancer: Add "超级个体" pathway analysis

Important Notes

  • All communication with the user is in Chinese (简体中文)
  • The HTML report is self-contained, using inline CSS and vanilla JavaScript (no external dependencies except Chart.js loaded from CDN)
  • Risk scores are estimates based on self-reported data — always include a disclaimer
  • Do NOT store user personal career data permanently; process in-memory only
  • The skill should be empathetic but direct — don't sugarcoat high-risk results
  • When the user is at high risk (>75), spend extra time on Phase 5 action planning
  • Always end with a concrete, actionable "本周行动" (This Week's Action)
Usage Guidance
Install only if you want a structured AI-era career assessment. Treat city and salary as optional, avoid sharing details you would not want included in a generated local HTML report, and remember the risk scores are estimates rather than professional career or compensation advice.
Capability Assessment
Purpose & Capability
The requested career profile, AI automation risk scoring, recommendations, and 12-month plan all match the stated purpose; optional city and salary fields are sensitive but relevant to tailoring advice.
Instruction Scope
The trigger phrases are broad and the workflow can ask for structured personal career information, so users should invoke it intentionally and skip optional questions they do not want to answer.
Install Mechanism
The artifact consists of Markdown instructions, reference Markdown files, and an HTML template; no executable installer, package install, credential requirement, or background setup is present.
Credentials
The skill reads its bundled references and template and writes a generated HTML report in the workspace, which is proportionate to its reporting function.
Persistence & Privilege
The instructions explicitly say not to store user career data permanently and to process it in memory only; no persistence, privilege escalation, or credential/session access is requested.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-career-planner
  3. After installation, invoke the skill by name or use /ai-career-planner
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
初始版本:六维AI风险评估+五维能力模型+18个AI新职业+三梯队推荐+12个月行动计划+HTML报告
Metadata
Slug ai-career-planner
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is AI时代职业规划助手?

AI时代职业规划助手。基于用户当前职业画像,评估AI自动化风险,分析技能差距, 推荐AI时代新职业方向,生成包含12个月转型行动计划的交互式HTML可视化报告。 覆盖技术/产品/运营/设计/市场/行政等核心岗位类别。 Triggers: 职业规划, AI时代职业, 职业转型, 未来职业, AI替代风险, 职业方向... It is an AI Agent Skill for Claude Code / OpenClaw, with 43 downloads so far.

How do I install AI时代职业规划助手?

Run "/install ai-career-planner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is AI时代职业规划助手 free?

Yes, AI时代职业规划助手 is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does AI时代职业规划助手 support?

AI时代职业规划助手 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created AI时代职业规划助手?

It is built and maintained by bettermen (@bettermen); the current version is v1.0.0.

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