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Academic Introduction Polisher

Community
GuangyaoYin
academic-introduction-polisher

Polish, translate, draft, or diagnose academic-paper Introduction sections, with emphasis on logical progression, paragraph functions, evidence-calibrated wording, and journal-ready English. Use for Chinese-to-English or English-to-English Introduction work; do not use for Methods, Results, Discussion, abstracts, or general document formatting unless the user also requests them.

Overview

PublisherGuangyaoYin
Repositoryskills
Skill nameacademic-introduction-polisher
Stars
64
Forks
2
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Academic Introduction Polisher 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/GuangyaoYin/skills/tree/main/skills/academic-introduction-polisher
  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/GuangyaoYin/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/academic-introduction-polisher .claude/skills/academic-introduction-polisher
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Introduction Polisher 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 Academic Introduction Polisher 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 Academic Introduction Polisher 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.

Academic Introduction Polisher

Improve only the Introduction unless the user explicitly expands the scope. Preserve the author's scientific meaning, evidence strength, citations, terminology, numerical values, and causal boundaries. Do not invent literature, findings, novelty, or stronger claims.

Choose the mode

  • Diagnose: identify paragraph roles, logic gaps, repetition, unsupported transitions, and claim-strength problems.
  • Polish: improve logic, cohesion, concision, academic tone, and sentence structure without changing substantive meaning.
  • Translate: produce idiomatic academic English rather than word-for-word English; preserve scientific relationships and uncertainty.
  • Draft/restructure: build or reorganize an Introduction from the user's evidence, citations, outline, or existing text.

When the request combines modes, diagnose the logic first, then translate or polish. Do not ask for a target journal unless its house style would materially change the result; otherwise default to concise, evidence-calibrated geoscience prose.

Core workflow

  1. Identify the paper's central phenomenon, established knowledge, precise gap, proposed solution, and contribution.
  2. Map each paragraph to one primary rhetorical job. Prefer a four- or five-paragraph research-article structure, but compress or expand when the journal genre warrants it.
  3. Repair logic before sentence-level polishing. Make every however, therefore, although, and here we express a real logical relation.
  4. Calibrate claim verbs to evidence: distinguish suggest, indicate, show, demonstrate, reveal, argue, and propose.
  5. Remove generic background, author-by-author literature lists, repeated claims, unsupported novelty, and method inventories that do not explain how the gap is closed.
  6. Check terminology, tense, subject continuity, parallel structure, modifier attachment, and citation placement.
  7. Return the polished or translated Introduction first. Follow with only the diagnostic notes or alternatives that materially help the user.

Non-negotiable writing rules

  • Prefer a concrete phenomenon or quantitative fact over a generic opening such as “Earthquakes are among the most devastating natural hazards.”
  • State a specific unknown variable, relationship, scale, or identifiability problem; do not use “few studies” as the sole gap.
  • Organize literature by scientific judgment or mechanism, not by a list of authors.
  • Explain why the data or method can resolve the stated gap; do not merely list tools.
  • Avoid direct questions in ordinary Introductions. Rewrite them as a precise knowledge gap or study objective. A single rhetorical question is acceptable only when the user explicitly wants a highly compressed Nature/Science-like voice and it improves the logic.
  • Use Further investigation indicates that ... only when it genuinely advances an earlier observation to a deeper result or interpretation.
  • Use show that for directly supported results, reveal that for a previously hidden pattern or relationship, and Our observations delineate ..., highlighting ... for structures, boundaries, distributions, or migration paths.
  • Treat “top-journal style” as compression, logical force, and evidential precision—not ornate vocabulary or exaggerated certainty.

References

Read only the references relevant to the user's mode. For a short sentence-level edit, the entrypoint may be sufficient.

Default output

For a full Introduction, provide:

  1. the revised English Introduction;
  2. a concise logic map by paragraph;
  3. a short list of consequential changes or claims needing author verification.

For translation-only requests, omit commentary unless ambiguity, terminology, or claim strength requires a note. If the source lacks evidence needed for a strong claim, preserve the limitation and flag it rather than silently repairing it with invented content.

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 Academic Introduction Polisher AI skill do?

Polish, translate, draft, or diagnose academic-paper Introduction sections, with emphasis on logical progression, paragraph functions, evidence-calibrated wording, and journal-ready English. Use for Chinese-to-English or English-to-English Introduction work; do not use for Methods, Results, Discussion, abstracts, or general document formatting unless the user also requests them.

Why use Academic Introduction Polisher on TypingMind?

Because you install it once and use it with any model. Academic Introduction Polisher 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 Academic Introduction Polisher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GuangyaoYin/skills/tree/main/skills/academic-introduction-polisher. 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 Academic Introduction Polisher?

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 Academic Introduction Polisher?

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

Is the Academic Introduction Polisher AI skill free?

It is published on GitHub by GuangyaoYin. 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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