wanyichen06/LLMInternSkill

LLMInternSkill: LLM internship resume and job-search Codex Skill for resume polish, JD tailoring, evidence guard, interview grilling, and Project Scout. 大模型实习简历与求职工具箱。

What it solves

LLMInternSkill addresses the problem of "inflated" or vague resumes for AI internship candidates. Instead of simply making a resume sound better, it ensures that every claim is backed by evidence, preventing candidates from being "exposed" during technical interviews while helping them translate raw technical work into professional, high-impact language.

How it works

The project operates as a toolkit (and a Codex Skill) that processes a candidate's raw resume, target job descriptions (JD), and a folder of supporting materials (code, notes, papers). It performs several key operations:

  • Evidence Auditing: It analyzes materials to determine if a claim is supported, needs to be downgraded (made more conservative), or requires more evidence.
  • Resume Polishing & Tailoring: It rewrites bullet points to be more technical and specific, and re-ranks experiences to match a specific JD.
  • Interview Simulation: It generates "grilling" questions based on the resume to prepare the candidate for deep technical questioning.
  • Project Scouting: If a candidate lacks sufficient evidence for a role, it recommends specific open-source projects to replicate or modify to build genuine skills and evidence.
  • LaTeX Export: It provides a professional LaTeX template to generate the final PDF resume.

Who it’s for

It is designed for students and early-career professionals seeking internships or jobs in the LLM and AI space, specifically those targeting roles in RAG, Agents, Post-training, Pretraining, and Multimodal AI.

Highlights

  • Evidence-Bound Approach: Prioritizes technical truth over "marketing speak" to ensure resume claims can withstand interview scrutiny.
  • Comprehensive Pipeline: Covers the entire journey from material audit and JD matching to interview prep and LaTeX formatting.
  • Targeted Role Support: Includes specific check-lists for various AI domains like Agentic RL, SFT/DPO, and Vector DBs.
  • Actionable Upgrade Plans: Provides tiered plans (1 day / 3 days / 1 week) to fill skill gaps through open-source project work.

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