General Interview Preparation Framework

Problem

Interview prep often stops at memorizing answers. Once a candidate has a target company and job description, it is hard to know which round is being tested, how to organize public evidence, and how to answer questions about market structure, revenue model, or AI monetization. This skill turns preparation into a repeatable workflow: confirm the role and JD, structure research around keywords, and generate staged Q&A with mock practice. It is useful for product, business analysis, industry research, and AI business roles.

How it works

• Intake: confirm the company, business line, JD keywords, interview stage, and weak areas before researching.
• Structured research: search multiple dimensions in parallel, cross-check sources, and prioritize official disclosures and industry reports. Data is labeled with ✓ official, △ third-party estimate, and ○ assumption.
• Framework building: turn answers into reusable structures such as competitive positioning, business-model formulas, value chains, AI disruption layers, and market-size estimates.
• Stage-aware questions: early rounds focus on market understanding and value judgment; later rounds focus on strategy, competition, sharp follow-ups, and role fit.
• Mock iteration: produce a full report, then simulate interview responses using 'position + counterpoint' and leave room for assumption correction.

Boundaries and notes

It suits interviews that require business judgment and structured reasoning, not pure coding, system design, or execution detail. Output quality depends on the company name, JD, and interview stage supplied by the user. When no direct competitor exists, the skill favors category definition, frontier references, and trust-building paths. When data is thin, it prefers conservative, base, and optimistic ranges with stated assumptions rather than false precision.