Install
openclaw skills install ai-career-plannerAI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, 转行建议, 技能提升, 职业生涯, career planning, AI career, job risk, 一人公司, 超级个体, 做什么不会被AI替代, 什么工作有前景
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 rolesreferences/risk_factors.md— Automation risk assessment framework & scoring methodologyreferences/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.mdbased 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
- Read
assets/report_template.htmlas the base template - 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
- Write the completed HTML to
ai-career-plan-{{TIMESTAMP}}.htmlin workspace - Present with
preview_url - Deliver with
deliver_attachments - 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)