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Office Academic Skill

CommunityPopular
zLanqing
office-academic-skill

Chinese-first academic Word and PowerPoint workflow for paper reading reports, thesis or group-meeting PPTs, editable DOCX/PPTX generation, Office file inspection, template matching, speaker notes, and layout quality checks. Use when the user asks to read papers into Word reports, create or polish PPT/PPTX, convert paper/thesis materials into slides, edit DOCX/PPTX, inspect Office files, or produce Chinese academic presentation/report deliverables. Preserve English paper titles, formulas, variable names, software commands, and references.

Overview

PublisherzLanqing
Repositorycodex-claude-academic-skills
Skill nameoffice-academic-skill
Stars
4K
Forks
222
Bundled files
122
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.

  • 122 bundled files

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

  • Open source

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

Installation

Install the Office Academic 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.
  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/zLanqing/codex-claude-academic-skills.git /tmp/codex-claude-academic-skills
mkdir -p .claude/skills
cp -r /tmp/codex-claude-academic-skills/office-academic-skill .claude/skills/office-academic-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Office Academic 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 Office Academic 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 Office Academic 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.

Office Academic Skill

Scope

Use this skill for:

  • Word reports from PDFs, DOCX files, arXiv papers, journal articles, theses, and manuscripts.
  • Chinese-first academic PPTs for literature reports, group meetings, courses, opening/midterm/defense presentations, and project presentations.
  • Editable .docx and .pptx generation, inspection, repair, and style preservation.
  • PPT template matching, native slide editing, speaker notes, and visual quality checks.

Do not use this skill for pure manuscript prose drafting without a Word/PPT deliverable; use research-writing-skill instead. Do not use it for MATLAB, Python analysis, statistics, or plotting unless those outputs are being inserted into Word/PPT.

Language And Evidence

  • Default to Chinese for explanations, Word report prose, slide text, outlines, and speaker notes.
  • Preserve English titles, formulas, variables, model names, software commands, reference entries, and direct source labels.
  • Distinguish 论文原文, 图表/公式证据, 代码或仿真结果, 根据上下文推断, and 建议.
  • Do not invent DOI, authors, journal details, experiment values, figure numbers, section names, page numbers, or conclusions.
  • Attach source labels to claims, parameters, quantitative results, formula explanations, datasets, figures, limitations, and novelty statements.

Paper Reading To Word

Default output, unless the user asks otherwise:

  1. A bilingual English-Chinese report for fast browsing.
  2. A Chinese-only report for submission, teaching, or presentation preparation.
  3. Optional Markdown working notes if useful.

Before writing, build a source map:

  • Title, authors, venue, year, DOI/arXiv if present.
  • Section headings and page spans when available.
  • Figures, tables, equations, datasets, hardware/software, and evaluation settings that support key claims.
  • Uncertain or missing metadata marked as 未在原文中明确给出.

Use references/report-structure.md for the default report structure and evidence-label format.

For .docx creation or editing:

  • Prefer structured headings, summary tables, figure/table placeholders, and source labels.
  • Use reliable Chinese fonts such as Microsoft YaHei or SimSun; use Times New Roman, Calibri, or Arial for English and numbers.
  • For existing academic/legal/business Word documents, make a new version or use tracked-change style edits rather than overwriting the original.
  • For advanced DOCX operations, use references/office-docx/ooxml.md, references/office-docx/docx-js.md, and the scripts under references/office-docx/.

Academic PPT Workflow

First clarify only the high-impact missing details:

  • Purpose: literature report, group meeting, course report, opening/midterm/defense, project display, science communication, or other.
  • Duration and slide count.
  • Audience and evaluation criteria.
  • Required template, school/company constraints, fonts, ratio, logo, sections, notes, or output format.
  • Source files: paper, thesis, Word draft, data, MATLAB/Python/Origin figures, screenshots, old PPT, template.

If the user asks to proceed immediately, make reasonable defaults and state them briefly.

For research PPTs, use a concise structure:

  1. Cover.
  2. Research background and problem.
  3. Related work or theoretical basis.
  4. Method, model, system, or algorithm.
  5. Experiment/simulation setup.
  6. Results and analysis.
  7. Comparison and discussion.
  8. Contributions, limitations, and outlook.
  9. Q&A.

For paper-reading PPTs, use:

  1. Paper metadata.
  2. Background.
  3. Core problem.
  4. Method framework.
  5. Experiment setup.
  6. Main results.
  7. Contributions.
  8. Limitations.
  9. Possible improvements.
  10. Relationship to the user's topic.

Slide Quality Rules

  • One core point per slide.
  • Prefer action titles that state the conclusion, not vague topic labels.
  • Figures, diagrams, tables, and formulas should carry the technical argument; avoid long paragraphs.
  • Keep axes, units, legends, formulas, assumptions, data sources, and figure captions scientifically accurate.
  • Use white or restrained academic backgrounds unless a supplied template requires otherwise.
  • Limit colors and decoration; use color to direct attention to evidence.
  • Avoid text overflow, image stretching, Chinese garbling, missing fonts, stale template text, bad navigation labels, and overlapping elements.

The academic-pptx repository was reviewed as an external reference. Because it marks its license as proprietary, do not copy its text into outputs or this skill. Use only general academic presentation principles: argument-first structure, action titles, evidence-led slides, and the ghost-deck test.

PPTX Technical Work

For template-matched defense PPTs:

  • Prefer copying native template slides and replacing content rather than rebuilding from blank slides.
  • On Windows with Microsoft PowerPoint installed, PowerPoint COM can be used for cloning, export, and overflow inspection.
  • Never modify the user's original PPTX directly. Work on a timestamped or versioned copy.
  • Do not disable PowerPoint add-ins or change application settings unless the user explicitly approves in that task.

Useful bundled resources:

  • references/thesis-defense-pptx/scripts/ for thesis context extraction, template cloning, slide export, contact sheets, text scans, and overflow inspection.
  • references/office-pptx/ for OOXML-level PPTX inspection and editing.
  • references/office-docx/ for OOXML-level DOCX inspection and editing.

Quality Gate

Before final delivery, verify what is feasible:

  • For Word: inspect extracted text or package XML for missing text, garbled Chinese, broken images, table overflow, and source labels.
  • For PPT: export or inspect slides, check page order, stale placeholders, text overflow, image aspect ratio, overlap, and readability.
  • Report output file paths, source paths, extraction method, checks performed, and unresolved uncertainties.

Bundled files

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

and 62 more files.

Frequently asked questions

What does the Office Academic Skill AI skill do?

Chinese-first academic Word and PowerPoint workflow for paper reading reports, thesis or group-meeting PPTs, editable DOCX/PPTX generation, Office file inspection, template matching, speaker notes, and layout quality checks. Use when the user asks to read papers into Word reports, create or polish PPT/PPTX, convert paper/thesis materials into slides, edit DOCX/PPTX, inspect Office files, or produce Chinese academic presentation/report deliverables. Preserve English paper titles, formulas, variable names, software commands, and references.

Why use Office Academic Skill on TypingMind?

Because you install it once and use it with any model. Office Academic 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 Office Academic Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zLanqing/codex-claude-academic-skills/tree/main/office-academic-skill. 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 Office Academic 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 Office Academic Skill?

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

Is the Office Academic Skill AI skill free?

Yes. It is published on GitHub by zLanqing 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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