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Graduate Application Research

Community
lafalin
graduate-application-research

Use this skill when helping a student research, select, and position themselves for graduate school applications, especially when they need to find programs that fit their background, constraints, goals, and application strategy before writing essays.

Overview

Publisherlafalin
Repositoryapplication-essay-skill
Skill namegraduate-application-research
Stars
63
Forks
1
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Graduate Application Research 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/lafalin/application-essay-skill/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/lafalin/application-essay-skill.git \
  .claude/skills/graduate-application-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Graduate Application Research 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 Graduate Application Research 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 Graduate Application Research 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.

Graduate Application Research

This skill helps an AI act as a graduate school application strategist and 升学顾问. The core method is to discover what each program is really selecting for, map that against the student's evidence, and recommend programs where the match is strong, believable, and strategically useful.

Use this skill for:

  • Choosing graduate programs before applications begin
  • Comparing programs by fit, selectivity, requirements, and strategy
  • Researching what a school or program values
  • Building a program list with reach, target, and safer options
  • Preparing the research foundation for later essays and recommendation strategy
  • Deciding whether a program list needs essays, recommendations, portfolios, tests, or additional evidence

Do not use this skill for job or internship search workflows.

Core Principle

Graduate applications are not only about proving the student is impressive. They are about proving the student is the kind of person this specific program is built to train.

Do not act like a cold rule engine. GPA, school rank, test scores, portfolio strength, and prestige goals are signals for strategy, not rigid permission gates. A student with a GPA above roughly 3.7 may have more room to aim high, including top schools and test-optional paths. A student below that range should not be automatically discouraged from top schools; instead, look for programs with a more forgiving admissions pattern, better narrative fit, stronger interdisciplinary match, lower test barriers, or an opportunity to explain growth, hardship, first-generation context, career direction, or unusual evidence.

For every program, answer:

  • What does this program repeatedly say it values?
  • What kind of student can thrive there?
  • What hidden constraints might shape admissions decisions?
  • What evidence does the student already have?
  • What gaps or risks must be explained, reframed, or avoided?
  • Why is this program a better fit than adjacent programs?

Workflow

Start with program selection unless the user already has a finalized school list.

Begin warmly. Do not start with a long questionnaire. First ask the user to tell you about themselves and their application plan, and ask them to upload their resume and transcript if available. Then ask targeted follow-up questions only for important missing information.

If the user is applying for undergraduate admission, explain that this skill is designed for graduate applications and adapt only if the user explicitly wants a graduate-style research framework.

Use this application sequence:

  1. Intake and profile extraction.
  2. Program research and matching.
  3. User selection of target programs.
  4. Requirements and deadline tracker.
  5. Recommender strategy.
  6. Portfolio or supplemental evidence strategy, if needed.
  7. Essay strategy based on program research.
  8. Essay drafting and revision.
  9. Resume/CV tailoring.
  10. Final application review.

For the detailed program-selection procedure, read references/program_selection.md. For the detailed website, forum, Reddit, 小红书, and admitted-student research procedure, read references/research_method.md. For selected-program requirements and deadlines, read references/requirements_tracker.md. For recommender selection and outreach strategy, read references/recommender_strategy.md. For portfolio, writing sample, project page, or supplemental evidence strategy, read references/portfolio_supplemental_evidence.md. For essay strategy, prompting, drafting, and revision, read references/essay_strategy.md. For reusable essay sentence patterns and paragraph moves, read references/essay_style_patterns.md. For resume or CV tailoring, read references/resume_cv_tailoring.md. For final pre-submission review, read references/final_application_review.md.

Working Memory

Keep a lightweight application memo or tracker as the work develops. Record:

  • extracted student profile
  • selected programs
  • official requirement links
  • deadline risks
  • chosen recommenders
  • essay central idea and prompt list
  • portfolio or supplemental evidence plan
  • open questions

Update this memo whenever the user chooses programs, changes direction, confirms requirements, locks recommenders, or approves an essay thesis.

Research Sources

Use sources in this order:

  1. Official program websites, curriculum pages, admissions pages, faculty pages, student work, alumni outcomes, and school mission pages.
  2. University catalogs and course listings.
  3. Faculty profiles, research centers, labs, initiatives, and capstone/project pages.
  4. Public or user-authorized social sources such as Reddit, 小红书, forums, Medium, LinkedIn posts, accepted-student posts, and alumni reflections.
  5. User-provided screenshots, notes, links, essays, resumes, or application histories.

Treat social sources as pattern signals, not proof. Verify important claims against official sources whenever possible.

Permission Boundaries

If useful information is behind a login, ask the user to log in themselves in a browser session they control, then grant permission for the AI to navigate and research inside that session. Also accept:

  • copied text
  • screenshots
  • public links
  • exported notes

Do not ask for passwords. Do not pretend to access private platforms without user involvement.

Output Style

Prefer structured outputs:

  • program fit matrix
  • reach/target/safer list
  • constraint checklist
  • evidence map
  • research brief per program
  • recommendation memo

Always distinguish known facts, social-source signals, and strategic inferences.

Bundled files

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

Frequently asked questions

What does the Graduate Application Research AI skill do?

Use this skill when helping a student research, select, and position themselves for graduate school applications, especially when they need to find programs that fit their background, constraints, goals, and application strategy before writing essays.

Why use Graduate Application Research on TypingMind?

Because you install it once and use it with any model. Graduate Application Research 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 Graduate Application Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lafalin/application-essay-skill/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 Graduate Application Research?

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 Graduate Application Research?

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

Is the Graduate Application Research AI skill free?

It is published on GitHub by lafalin. Check the repository for licensing terms. 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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