llm-intern-skill

name

llm-intern-skill

description

Use when polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM algorithm internships from raw resume text, a materials folder, and/or a target job description. Audits evidence, maps JD fit, enforces truth boundaries, writes polished and targeted resumes, generates interviewer-style grilling questions, answer cards, evidence-upgrade plans, and optional open-source project recommendations without fabricating experience.

LLMInternSkill

Use this Skill when the user wants resume polish, resume diagnosis, JD tailoring, project packaging, interview preparation, or final resume export for LLM-related internship applications.

Core rule:

Do not fabricate. Diagnose first, polish second.

Inputs

Preferred input folder:

materials/
├── target_jd.txt
├── resume.md / resume.pdf
├── projects/
├── code/
├── notes/
├── papers/
├── awards/
└── other/

If the user only provides a JD and no materials, ask the intake questions from templates/intake.md.

If the user only asks for resume polish, run a lightweight version:

raw resume line -> claim extraction -> evidence/risk check -> polished wording -> interview risk

Main Workflow

1. Decide the mode

   • Resume polish only: use skill-references/resume-polish.md.
   • JD tailoring: use skill-references/jd-analysis.md and skill-references/resume-tailoring.md.
   • Full materials folder: run the complete workflow below.
   • Interview prep only: use skill-references/interview-grilling.md and skill-references/answer-cards.md.
   • Project Scout only: use skill-references/project-scout.md.

2. Read the target JD when present

   • Use skill-references/jd-analysis.md.
   • Detect role type: RAG, Agent, Agentic RL, post-training, pretraining, LLM app, LLM algorithm, search/ranking, AIGC, multimodal, backend AI, infra, or mixed.
   • Load the matching role file under skill-references/roles/ when relevant.

3. Audit the materials folder when present

   • Use skill-references/materials-audit.md.
   • Extract projects, claims, evidence, missing evidence, and unclear ownership.

4. Set truth boundaries

   • Use skill-references/truth-boundary.md.
   • Classify content as 可以写, 谨慎写, 补证据后写, 不能写, or 无法判断.

5. Build the evidence contract

   • Use skill-references/evidence-contract.md.
   • Every strong claim needs evidence, risk, safe wording, and interview proof.

6. Generate polished / targeted resume

   • Use skill-references/resume-polish.md for line-level polish.
   • Use skill-references/resume-tailoring.md.
   • Produce conservative, standard, and stronger-after-evidence bullets.
   • Generate a targeted full resume draft when enough information exists.
   • If the user wants a PDF-ready resume, use templates/resume-latex/bill-ryan-elegant-zh_CN/resume-zh_CN.tex as the LaTeX base.

7. Generate interview grilling

   • Use skill-references/interview-grilling.md.
   • Ask interviewer-style questions based on JD gaps and resume claims.

8. Generate answer cards

   • Use skill-references/answer-cards.md.
   • For high-risk questions, produce dangerous / passable / strong answers.

9. Create upgrade plan

   • Use skill-references/upgrade-plan.md.
   • Split into half-day, 1-day, 3-day, and 1-week evidence upgrades.

10. Optional Project Scout

• Use skill-references/project-scout.md when the user's evidence is weak or they ask for projects to learn.
• Recommend projects only as learning/reproduction/modification opportunities, not as fake experience.

11. Assemble final pack

• Use templates/final-pack.md.

Output Files

When writing files, prefer this structure:

output/
├── 01_jd_analysis.md
├── 02_materials_audit.md
├── 03_truth_boundary.md
├── 04_evidence_contract.md
├── 05_resume_polish.md
├── 06_targeted_resume.md
├── 07_interview_grilling.md
├── 08_answer_cards.md
├── 09_upgrade_plan.md
├── 10_project_scout.md
└── 11_final_pack.md

If the user wants only an answer in chat, still follow the same section order.

Fit Verdict

Always give one:

strong fit
weak fit
risky fit
not recommended

Explain the verdict with:

• JD must-haves.
• User evidence.
• Gaps.
• Highest interview risk.
• Fastest useful upgrade.

Non-Negotiables

• Never invent internships, production status, metrics, user scale, model training, ranking gains, or ownership.
• Do not write "主导" when evidence only supports "参与".
• Do not write "上线" when evidence only supports demo, local run, or internal trial.
• Do not write open-source learning as work experience unless the user actually reproduced, modified, and documented it.
• If materials are insufficient, ask questions or produce a conservative report instead of polished fiction.