Install
openclaw skills install @harrylabsj/career-path-advisorCareer Path Advisor
Analyze your skills versus market demands to identify gaps, explore career routes, and create personalized learning roadmaps for targeted role transitions.
Career Path Advisor (职业路径顾问)
Turn career anxiety into an actionable plan. Analyze your current skill stack against market demands, identify gaps, and generate personalized career roadmaps with concrete learning milestones — all grounded in real job market data, not generic advice.
Prerequisites
The CLI script requires two common tools:
| Tool | Purpose | Install (macOS) | Install (Ubuntu/Debian) |
|---|---|---|---|
| jq | JSON processing (role matching, JSON output) | brew install jq | sudo apt install jq |
| python3 | Dynamic role matching, gap analysis computation | Ships with macOS | sudo apt install python3 |
Both are pre-installed on most development machines. Verify with:
jq --version
python3 --version
Core Capabilities
- Skill Radar Mapping: Inventory your current skills and visualize strengths vs gaps with a structured radar chart
- Market-Driven Gap Analysis: Scrape target role JDs to extract real, current skill demands — not textbook lists
- Multi-Path Generation: Produce 2–4 viable career trajectories (lateral move / promotion / industry switch / functional pivot)
- Learning Roadmaps: Per-path timeline with milestones, recommended resources (courses, books, projects), and completion estimates
- Offer Comparison Engine: Decision matrix for evaluating multiple job offers across compensation, growth, culture, and risk
- Industry Transition Planner: Special handling for career changers — transferable skill mapping and bridge-role recommendations
- Salary Benchmarking: Market salary ranges by role, city, and experience level based on current job listings
Workflow (9 Steps)
Step 1: Current State Capture
Input: User provides:
- Current role & title (e.g., "Java后端开发, 高级工程师")
- Years of experience (e.g., 5 years)
- Industry (e.g., "互联网金融", "跨境电商")
- Core skills (free-form: "Spring Boot, MySQL, Redis, 微服务")
- Education & certifications (optional)
- Location (for salary benchmarking)
- Target direction (optional — e.g., "想转AI", "想做技术管理", "还在探索")
Output: Structured career profile. Logic: If target is "还在探索", move to Step 2 exploration mode. Otherwise skip to Step 4 for gap analysis.
Step 2: Career Exploration (if no target)
Input: Career profile with no target direction. Action: Based on current skills + experience + industry trends, generate 5-7 possible directions:
- Ladder-up: Senior/Staff/Principal IC within same track
- Management pivot: Tech lead → Engineering Manager
- Adjacent role: Backend → SRE, Backend → Data Engineer
- Industry switch: Fintech → E-commerce, Gaming → Enterprise SaaS
- Radical pivot: Engineer → Product Manager, Developer → Technical Writer
Output: Exploration menu with:
- Direction name + title examples
- Estimated difficulty (Easy / Moderate / Hard)
- Time to transition (3m / 6m / 12m+)
- Salary impact (↑ / ≈ / ↓)
- Match score with current skills
Logic: Flag unrealistic directions (e.g., 2 years experience → CTO).
Step 3: Target Role Selection
Input: Exploration menu (or user-provided target). Action: User selects 1-3 target directions. Confirm with clarifying questions:
- Preferred company tier (大厂/中厂/创业公司)?
- Willing to relocate?
- Salary floor?
- Hard constraints (e.g., "不接受996", "必须在上海")?
Output: Confirmed target role(s) with user constraints.
Step 4: Market Data Collection
Input: Target role(s) + location + company tier preference. Action: Scrape current job listings (Lagou, BOSS Zhipin, LinkedIn) for target roles:
- Extract top 20 JDs per target role
- Aggregate: required skills, preferred skills, experience range, salary range
- Identify trending skills (skills appearing in 2026 JDs but not 2024 JDs)
- Note: certificates, tools, and domain knowledge frequently listed
Output: Market demand dataset:
Target: AI Engineer (Shanghai, 3-5yr exp)
Demand: 850+ open positions
Salary Range: ¥30K–60K/month
Top Required Skills: Python (98%), PyTorch (82%), Transformer (71%), MLOps (58%), CUDA (45%)
Trending (↑): LangChain (240% YoY), Vector DB (180% YoY)
Step 5: Skill Gap Analysis
Input: Current skill stack + market demand dataset. Action: Map current skills against market requirements:
| Skill | Your Level | Market Demand | Required Level | Gap |
|---|---|---|---|---|
| Python | ⭐⭐⭐⭐ | Critical | ⭐⭐⭐⭐ | ✅ No gap |
| PyTorch | ⭐⭐ | Critical | ⭐⭐⭐⭐ | 🔴 Large gap |
| MLOps | — | Important | ⭐⭐⭐ | 🔴 Missing |
| Docker | ⭐⭐⭐ | Nice-to-have | ⭐⭐ | ✅ Exceeds |
Gap Score: Weighted by demand criticality × gap size. Output: Gap matrix + radar chart visualization (Mermaid/text).
Step 6: Path Planning
Input: Confirmed target + gap matrix + user constraints. Action: Generate 2–4 concrete paths:
Path A: Direct Apply (最快)
- Suitable when gap is small (<30% new skills needed)
- Timeline: 1–3 months (resume polish + interview prep)
- Action items: Update resume, practice system design, apply to 20+ positions
Path B: Skill-Build then Apply (最稳)
- Suitable when gap is moderate (30–60% new skills needed)
- Timeline: 3–6 months (learning + project building + apply)
- Action items: Complete 2 courses, build 1 portfolio project, network with target companies
Path C: Bridge Role (渐进)
- Suitable when gap is large (>60% new skills) or industry-switching
- Timeline: 6–12 months
- Action items: Take adjacent role first, learn on the job, internal transfer or re-apply
Path D: Parallel Exploration (探索)
- Try 2 directions simultaneously with low commitment
- Timeline: 2–4 months to decide
- Action items: Side projects, informational interviews, part-time consulting
Output: Per-path summary with timeline, milestones, effort estimate (hours/week), and success probability.
Step 7: Learning Roadmap Generation
Input: Selected path + gap matrix. Action: Generate a week-by-week or month-by-month learning plan:
- Month 1: Foundation courses (Coursera, B站, 极客时间)
- Month 2: Hands-on projects (build X, contribute to Y)
- Month 3: Advanced topics + interview prep
- Resources: Specific course names, book titles, GitHub repos, Chinese/English learning materials
- Checkpoints: "By end of Month 1, you should be able to..."
Output: Detailed learning roadmap with estimated hours per week and resource links.
Step 8: Offer Decision Matrix (if applicable)
Input: 2+ job offers with details (salary, equity, team, growth, location, culture). Action: Build a weighted decision matrix:
| Dimension | Weight | Offer A | Offer B | Offer C |
|---|---|---|---|---|
| Base Salary | 25% | ¥45K | ¥38K | ¥50K |
| Equity/Stock | 15% | Options | RSUs | None |
| Career Growth | 20% | Fast track | Stable | Unknown |
| Team & Culture | 15% | Strong | Risk | Good |
| Location/Commute | 10% | 30min | Remote | 60min |
| Work-Life Balance | 10% | 996 rumor | 1075 | Flexible |
| Company Stability | 5% | Series C | Public | Series A |
Output: Weighted scores + sensitivity analysis (how ranks change if you weight growth higher than salary). Risk flags per offer.
Step 9: Final Report
Input: All analysis results. Action: Compile the Career Development Plan:
- Current State: Skill radar + strengths/weaknesses
- Target Analysis: Market demand + salary benchmark
- Gap Analysis: What you need to learn, ranked by priority
- Recommended Path: Top pick with rationale
- Learning Roadmap: Month-by-month plan with resources
- Action Items: This week / this month / this quarter
- Progress Tracking: Suggested monthly check-ins
Output: Complete career development plan in Markdown. Option to track progress in follow-up sessions.
Sample Prompts
Prompt 1: Career Exploration
User: "我是做Java后端的，5年经验，感觉遇到了瓶颈。帮我看看有哪些发展方向" Expected Output: Exploration menu with 5-7 directions (Senior/Staff IC, Tech Lead/Manager, SRE/DevOps, Data Engineer, Solution Architect), each with difficulty, timeline, salary impact, and match score. Top 3 picks with rationale.
Prompt 2: Specific Role Transition
User: "我想从后端开发转AI工程师，帮我分析需要学什么，大概要多久" Expected Output: Market data for AI Engineer roles → gap matrix (Python: no gap, PyTorch: large gap, MLOps: missing) → 3 paths (Direct/6-month/12-month) → learning roadmap with specific courses and projects.
Prompt 3: Offer Comparison
User: "收到3个offer：字节跳动45K、一家B轮创业公司50K+期权、外企38K但轻松。帮我分析怎么选" Expected Output: Decision matrix with weighted dimensions → total scores → "If you prioritize growth: ByteDance (82). If you prioritize WLB: Foreign company (78). If you prioritize upside: Startup (75 but high variance)."
Prompt 4: Industry Switch
User: "我在传统制造业做IT，想转互联网行业。应该怎么切入？" Expected Output: Transferable skill mapping → bridge role recommendations (e.g., "先用制造业+IT的经验切入工业互联网/智能制造赛道") → learning gap analysis → 6-month transition plan.
Prompt 5: First Job Planning
User: "应届生，计算机专业，不知道该选前端/后端/算法/测试。帮我分析一下" Expected Output: Per-direction analysis: market demand (current + projected), salary curves, learning difficulty, career ceiling, remote work potential. Personalized recommendation based on user's interests (ask if unknown).
Prompt 6: Career Plateau Check
User: "工作7年了还是高级工程师，感觉升不上去。帮我诊断一下问题" Expected Output: "Career plateau diagnosis": ① Technical depth vs breadth analysis ② Visibility check (internal impact, external presence, mentoring) ③ Market comparison (do you have Staff-level skills?) ④ Action plan: specific changes to make in next 6 months.
Real Task Examples
Example 1: Mid-Career Pivot
Scenario: 30-year-old frontend developer, worried about AI replacing frontend work. Input: "我做了6年前端，React/Vue/TypeScript都很熟。但AI写前端越来越强，我是不是该转行？如果要转，转什么？" Steps:
- Profile: 6yr Frontend, strong JS/TS, experienced in UI architecture.
- Exploration: 5 directions generated.
- Market data: Frontend demand still strong but shifting — "full-stack frontend" (Next.js, edge computing) growing. AI-assisted frontend engineering is new niche.
- Gap analysis: To full-stack: need backend basics (Node.js already known → gap small). To AI: large gap.
- Recommendation: Path A (evolve to Full-Stack/Architecture) 3-6 months vs Path B (pivot to AI Engineering) 12 months. Output: "不建议完全转行。前端的终点不是被AI替代，而是成为'AI-Native前端架构师'。6个月学习计划: Next.js App Router → Edge Computing → AI SDK integration → 成为团队里最懂AI的前端。"
Example 2: Offer Decision
Scenario: Senior engineer with competing offers, torn between growth and stability. Input: Offer comparison with detailed numbers. Steps:
- Quantify each dimension with user-assigned weights.
- Calculate total weighted scores.
- Sensitivity analysis: "If you weight career growth at 30% instead of 20%, ranks change..."
- Hidden factors: equity liquidity, company runway, team churn rate (ask user to research).
- "Gut check" prompt: "Imagine you've been at Company A for 6 months. How do you feel?" Output: Decision matrix with clear recommendation + key assumptions to verify before accepting.
Example 3: Return-to-Work
Scenario: Parent returning to workforce after 2-year gap. Input: "休息了2年带娃，之前做了4年的产品经理。现在想回去工作，但感觉脱节了。" Steps:
- Profile: 4yr PM experience, 2yr gap, target = PM again.
- Market scan: PM landscape changes in 2 years (AI PM, Growth PM niches emerged).
- Gap: General PM skills intact. Gaps in: AI/LLM product knowledge (new trend), latest tools (Figma, Notion are standard now).
- Path: 2-month ramp-up → apply. Learning plan: AI PM fundamentals + build case study. Output: "2年gap不是劣势，把它重新定义为'高强度项目管理经验'。2个月回归计划: Week 1-2行业更新阅读 → Week 3-4项目实战 → Week 5-8投递面试。建议先从B轮-C轮公司切入，比大厂更容易接受gap。"
Scripts
| Path | Description |
|---|---|
scripts/advisor.sh | Main CLI script — profile-based or interactive career analysis |
schemas/input.schema.json | JSON Schema for workflow input (career profile) |
schemas/output.schema.json | JSON Schema for workflow output (plan, gaps, paths) |
references/roles.json | Role categories, transferable skills, and difficulty tiers |
CLI Usage
# Gap analysis with target role
bash scripts/advisor.sh --profile '{"current_role":"Java后端","years":5,"skills":["Spring Boot","MySQL","Redis"],"target":"AI工程师","location":"上海"}'
# Exploration mode (no target specified)
bash scripts/advisor.sh --profile '{"current_role":"前端开发","years":3,"skills":["React","TypeScript"],"location":"北京"}'
# Interactive mode
bash scripts/advisor.sh --interactive
# JSON output for programmatic use
bash scripts/advisor.sh --profile '{"current_role":"Java后端","years":5,"target":"AI工程师","location":"上海"}' --output json
🚀 First-Success Path (3 Steps)
- Step 1: Run
bash scripts/advisor.sh --profile '{"current_role":"Java后端","years":5,"location":"上海"}'or--interactive - Step 2: Review the exploration menu and pick 1-2 target directions
- Step 3: Run with
--targetto receive a detailed gap analysis and learning roadmap under 30 seconds
# Quick start
bash scripts/advisor.sh --profile '{"current_role":"Java后端","years":5,"target":"AI工程师","location":"上海"}'
Boundary Conditions
| Condition | Behavior |
|---|---|
| User has <1 year experience | Switch to "entry-level" mode; focus on industry/market insights, not deep gap analysis |
| User has >15 years experience | Switch to "executive" mode; emphasize leadership, strategy, board roles over IC skills |
| Target role does not exist in market data | Flag as "emerging/niche role"; use adjacent role data + trend extrapolation |
| Salary expectations unrealistic (>90th percentile for experience) | Flag with market data; suggest negotiation tips or tier adjustment |
| User in non-tech field (e.g., finance, healthcare) | Adapt framework; scrape industry-specific job boards |
| Multiple simultaneous targets requested | Cap at 3; suggest sequential exploration |
| Location outside China | Use LinkedIn/Indeed for market data; adapt to local norms |
| User requests "guaranteed" outcome | Redirect: "Career planning increases probability, not certainty. Here's your best path forward." |
Error Handling
| Error Code | Scenario | Handling |
|---|---|---|
| E-MARKET-DATA-FAIL | Job boards inaccessible | Fall back to cached/general market knowledge; flag as "estimated" |
| E-TOO-VAGUE | Career profile too thin (<3 skills listed) | Ask probing questions; suggest self-assessment exercise |
| E-UNREALISTIC-TARGET | Target requires 10+ years of unaccounted experience | Flag politely; suggest bridge roles or longer timeline |
| E-OFFER-INCOMPLETE | Offer comparison missing key dimensions | Ask for missing info; note assumptions made |
| E-RAPID-CHANGE | Target field changing too fast (e.g., crypto 2025) | Warn of high uncertainty; suggest shorter planning cycles |
| E-REGION-MISMATCH | Salary benchmarks unavailable for small city | Use nearest Tier-1 city data with cost-of-living adjustment |
Security Requirements
- No identity storage: Career profiles and skill data processed in-session only; not persisted across sessions
- No PII collection: Do not ask for real name, ID number, current employer name (use generic: "某大厂", "金融科技公司")
- Market data attribution: Cite data sources (e.g., "基于2026年6月BOSS直聘数据"). Note data freshness.
- No guarantee of outcomes: Always include disclaimer: career planning increases probability of success; it does NOT guarantee job offers, salary increases, or career satisfaction
- Salary data privacy: Salary benchmarks are aggregated anonymized market ranges, not individual data points
- Avoid insider information: Do not scrape or share non-public compensation data, internal promotion criteria, or proprietary company information