Interview Prep Assistant
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About this skill
What It Addresses
Interview prep often fails because question lists are broad, fragmented, and hard to review. 面经助手 organizes online interview reports for a target role into an actionable study path instead of a single long list.
How It Works
- Search: favors patterns like
{role} {company} {round} interview report, cross-checking sources from Tencent, ByteDance, Alibaba, and others. - Plan: creates
learning_plan.md, splitting large topics intosub_topicunits ordered by importance. - Push: each pass focuses on one subtopic and returns 1 knowledge point plus 10 interview questions, with progress stored in
state.json. - Review: later passes sample earlier questions for brief recall and update the plan every third call based on fresher trends.
Boundaries
It depends on live web search results and labels [AI-generated answer] when missing; it supports long-term interview review but does not replace company-specific assessments, project specifics, or hands-on code validation.
Use Cases
- Preparing for an LLM algorithm engineer interview, break ByteDance and Alibaba technical questions into topics and review daily.
- Job hunting for a Go backend role, get subtopic-level questions on concurrency and networking plus earlier-question recall.
- Preparing as a xinchuang project manager, turn hiring requirements, project management, and second-round questions into a structured plan.
- Preparing for a product manager interview, receive a scheduled subtopic with 10 questions and a brief review.
Best For
- Engineers targeting LLM algorithm roles who need to break down major-company questions into topics and subtopics.
- Go backend interview candidates who want subtopic pushes on concurrency, networking, and related interview questions.
- Project management candidates moving into xinchuang roles who need hiring requirements and second-round questions into a plan.
- Product manager interview seekers who want daily scheduled subtopic review and recall of earlier questions.
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