YingaoWang-casia

Helps users upgrade real resumes, internships, small-company projects, toy demos, Agent/LLM apps, data-cleaning tasks, internal tools, CRUD systems, frontend/backend projects, speech projects, or algorithm projects into credible big-tech interview-ready narratives. Use when the user wants JD-to-resume mapping, evidence-bound resume rewriting, weak-project diagnosis, large-company technical interview drills, interviewer-style deep dives, bad-case discovery, contribution boundary clarification, truthful project enhancement plans, or SP/SSP-level internship preparation without fabricating experience.

Uses local InterviewProof-RAG evidence packs to run grounded big-tech mock interviews, question-tree drills, answer grading, and project gap analysis without requiring API calls. Use when the user provides a codex_pack markdown file generated by InterviewProof-RAG.

computer science research lineage mapping, novelty discovery, and idea stress-testing for codex. use when the user wants to map extremely similar papers in a research direction, extract each paper's innovation points, generate paper innovation ideas, analyze whether an idea is reasonable, find CS research gaps, analyze a research direction or seed paper, review recent or ten-year literature trends, rank weak/medium/strong novelty ideas, or judge whether an idea is paper-ready. the skill supports two primary outputs: literature-lineage mode for detailed paper-context mapping, and idea-generation mode for concrete novelty candidates. it must ground claims in papers and ask clarifying questions when scope is underspecified instead of blindly searching broadly.