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Llm Intern Skill

Community
wanyichen06
llm-intern-skill

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.

Overview

Publisherwanyichen06
RepositoryLLMInternSkill
Skill namellm-intern-skill
Stars
332
Forks
15
Bundled files
104
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 104 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wanyichen06 on GitHub. Read the source before you install it.

Installation

Install the Llm Intern Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wanyichen06/LLMInternSkill/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanyichen06/LLMInternSkill.git \
  .claude/skills/llm-intern-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Llm Intern Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Llm Intern Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Llm Intern Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

text
Do not fabricate. Diagnose first, polish second.

Inputs

Preferred input folder:

text
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:

text
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.
  1. Assemble final pack
  • Use templates/final-pack.md.

Output Files

When writing files, prefer this structure:

text
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:

text
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 44 more files.

Frequently asked questions

What does the Llm Intern Skill AI skill do?

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.

Why use Llm Intern Skill on TypingMind?

Because you install it once and use it with any model. Llm Intern Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Llm Intern Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanyichen06/LLMInternSkill/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Llm Intern Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Llm Intern Skill?

As many as you like. As long as a model supports skills, you can use Llm Intern Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Llm Intern Skill AI skill free?

Yes. It is published on GitHub by wanyichen06 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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