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}}

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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)

text

【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

text

【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

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【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)

text

【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".

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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 FactorWeightScoring 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

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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

#CompetencyDescriptionWeight1AI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

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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

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Phase 5: Action Plan Generation#

5.1 12-Month Transition Roadmap

Generate a 4-quarter plan:

QuarterFocusKey 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

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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:

PlaceholderSourceDescription{{USER_NAME}}Derived or "职场人"User identification{{CURRENT_ROLE}}Q1Current job title{{INDUSTRY}}Q2Industry{{YEARS_EXP}}Q3Years of experience{{RISK_SCORE}}Phase 2 calculation0-100 risk score{{RISK_LEVEL}}Risk score threshold低/中等/较高/高风险{{RISK_LEVEL_CLASS}}CSS classlow/medium/high/critical{{RISK_BREAKDOWN}}Generated HTMLPer-factor risk breakdown{{TASK_ANALYSIS}}Generated HTMLTask-level risk table{{AI_READY_INDEX}}Phase 3 calculation0-100 index{{COMPETENCY_RADAR_DATA}}Phase 3 scoresJavaScript radar chart data{{COMPETENCY_BARS}}Generated HTML5-dimension bar visualization{{TIER1_RECOMMENDATIONS}}Generated HTMLUpgrade-in-place recommendations{{TIER2_RECOMMENDATIONS}}Generated HTMLAdjacent transition roles{{TIER3_RECOMMENDATIONS}}Generated HTMLNew track options{{QUARTERLY_PLAN}}Generated HTML4-quarter action plan{{LEARNING_RESOURCES}}Generated HTMLRecommended resources{{REPORT_DATE}}Current dateYYYY-MM-DD{{SCORE_CHART_JS}}Generated JSGauge 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

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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

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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)