Offer Radar: Real-Time Job Trends & Career Strategy Planner

Problems It Solves

Job seekers often need comparable evidence before choosing roles, cities, skills, or offers: whether demand for a role is rising, which directions fit their background, and how to weigh state-owned firms, private companies, startups, and public-sector paths. This skill turns those questions into market signals, personal positioning, learning routes, and execution plans instead of generic “learn hot tech” advice.

How It Works

• Market radar: For a target role, city, seniority level, and employer type, it uses public job listings or user-supplied materials to extract fields such as role name, salary band, required skills, and risk signals, then builds views on demand, salary, skills, and cities.
• Route planning: It starts with a short 8-question intake covering identity, background, location, salary floor, risk preference, timeline, and existing materials, then outputs conservative, growth-oriented, and exploratory routes with 30/60/90-day plans.
• Learning and projects: It scores tech stacks by demand, salary premium, entry barrier, portfolio potential, trend, and long-term compounding, and separates job-ready basics, project proof, and long-term moats to avoid vague “learn AI” advice.
• Offer and execution: For multiple offers, it applies weighted scoring across cash, growth, stability, city, role centrality, and contract risk, then provides negotiation language, interview prompts, funnel metrics, and “next 3 actions.”

Boundaries

The skill avoids inventing real-time conclusions when data is weak, separates facts from judgments and advice, and does not ask users to bypass login walls, captchas, paywalls, or anti-scraping controls, or to provide sensitive data such as ID numbers or bank accounts. It fits job research, career switching, new-graduate or experienced hiring, offer comparison, and long-term planning, but it does not replace company due diligence, legal contract review, or personalized financial advice.