Document Docx logo

Document Docx

Community
vasilyu1983
document-docx

Create/edit .docx files with styles, templates, comments, and extraction workflows. Use when asked to generate Word reports, contracts, proposals, or convert Word content.

Overview

Publishervasilyu1983
RepositoryAI-Agents-public
Skill namedocument-docx
Stars
87
Forks
19
Bundled files
22
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.

  • 22 bundled files

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

  • Open source

    Published by vasilyu1983 on GitHub. Read the source before you install it.

Installation

Install the Document Docx 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/vasilyu1983/AI-Agents-public.git /tmp/AI-Agents-public
mkdir -p .claude/skills
cp -r /tmp/AI-Agents-public/frameworks/shared-skills/skills/document-docx .claude/skills/document-docx
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Document Docx 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 Document Docx 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 Document Docx 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.

Document DOCX Skill - Quick Reference

This skill covers creation, editing, review, extraction, and release workflows for .docx documents.

Modern best practices (Jul 2026):

  • Treat .docx as the editable source and PDF as a release artifact.
  • Prefer templates and built-in styles over manual formatting.
  • Use comments for review notes; use Word Compare for true redlines.
  • For LLM/RAG extraction, optimize for structure, trust level, and sanitization rather than visual fidelity.
  • Treat macro-enabled Office files (.docm, .dotm) as untrusted by default.
  • Before promising a feature (comments, alt text, tracked changes), check the installed library version — several of these APIs are recent additions and silently absent on older pins. See "Version-Gate Before Promising A Feature" below.

Core Decision Rules (2026)

  • If non-developers need to own layout/design, prefer docxtpl with a Word-authored template.
  • If the stack is Python and edits are structural, prefer python-docx.
  • If the stack is TypeScript/Node and the output is generated server-side, prefer docx.
  • If you need semantic HTML from a trusted document, prefer mammoth, then sanitize before rendering or storing the output.
  • If you need Markdown/JSON for search, indexing, or RAG, prefer MarkItDown or Docling.
  • If the user asks for tracked changes, do not promise high-level library support. Generate a revised .docx and use Word Compare, or switch to OOXML-specialized tooling.
  • If the user asks for PDF output, prefer Word automation for highest fidelity and LibreOffice headless for cross-platform batch workflows.
  • If the input is .doc, convert to .docx first. If it is .docm or .dotm, do not trust embedded macros.

Quick Reference

TaskTool/LibraryLanguageWhen to Use
Create/edit DOCXpython-docxPythonStructural edits, reports, contracts, section/table/image work
Create/edit DOCXdocxNode.jsServer-side generation in TypeScript-heavy stacks
Template filldocxtplPythonWord-authored templates, mail merge, batch documents
Add/access commentspython-docx + Word review workflowPython / WordReview notes without tracked revisions
Convert DOCX to HTMLmammothNode.jsSemantic HTML from trusted documents
Convert DOCX to MarkdownMarkItDownPythonLLM/RAG ingestion where Markdown is preferred
Convert DOCX to Markdown/HTML/JSONDoclingPython / CLIMulti-format ingestion, structured extraction, batch conversion
Parse text/tables/metadatapython-docx + OOXML inspectionPythonExtraction, audits, migration tooling
Parse tracked changes/commentsOOXML, Open XML SDK, docx4j, Aspose.WordsPython / .NET / JavaRevision-heavy workflows and interoperability edge cases
Convert DOCX to PDFWord automation / LibreOffice headlessOS toolingRelease artifacts and cross-platform smoke checks

Selection Guide

  • Prefer docxtpl when a legal, ops, or business user needs to maintain the template in Word.
  • Prefer python-docx for moderate formatting complexity where you control the document structure in code.
  • Prefer docx when the surrounding service and tests already live in Node.js.
  • Prefer mammoth for trusted, text-first conversion to HTML; it is not a fidelity-preserving renderer.
  • Prefer MarkItDown for simple DOCX-to-Markdown pipelines.
  • Prefer Docling when DOCX is only one input among many formats or you need HTML/JSON/Markdown/text output from a unified pipeline.

ASCII Flow

text
DOCX request
  |
  v
Classify file + trust level
  |-- .docx / .dotx -----> normal OOXML workflow
  |-- .doc -------------> convert to .docx first
  |-- .docm / .dotm ----> treat macros as untrusted
  |
  v
Choose lane
  |-- Word-owned template ------> docxtpl
  |-- Python structural edit ---> python-docx
  |-- Node service generation --> docx
  |-- trusted HTML conversion --> mammoth + sanitizer
  |-- Markdown / JSON ingest ---> MarkItDown or Docling
  |-- tracked-change review ----> revised DOCX + Word Compare
  |
  v
Generate, edit, or extract
  |
  v
Quality gate
  |-- parseability + unresolved tags ---> scripts/docx_quality_gate.py
  |-- comments / revisions / OOXML ----> scripts/docx_inspect_ooxml.py
  |
  v
Viewer, accessibility, and release checks

Format And Safety Caveats

  • .docx and .dotx are Office Open XML packages; .doc is legacy binary and needs conversion first.
  • .docm and .dotm are macro-enabled; do not treat them as safe content inputs.
  • python-docx can add and read comments in the main document body, but not threaded replies/resolved states, and not comment anchors in headers/footers.
  • python-docx does not provide reliable tracked-change authoring.
  • mammoth performs no sanitization of generated HTML or links from untrusted source documents.
  • Tables of contents and many Word fields are placeholders until updated in Word.

Version-Gate Before Promising A Feature

Do not assume the environment has a current library. This is the single most common way this skill causes a confident-but-wrong answer:

  • Document.add_comment() only exists from python-docx 1.2.0 onward. On an older pinned version it raises AttributeError, not a graceful fallback. Check first: python -c "import docx; print(docx.__version__)".
  • python-docx still has no public high-level property for image alt text (no .alt_text on InlineShape) as of the current 1.x line — InlineShape only documents height, width, and type. The OOXML workaround in references/accessibility-compliance.md reaches into the private _inline attribute; treat that as an implementation detail that can move between releases, re-verify after any python-docx upgrade, and prefer python-docx's own comment/style APIs wherever a public one exists instead of private attributes.
  • If a user asks for a feature this skill flags as unsupported (tracked-change authoring, threaded comment replies, resolved-state comments), say so plainly rather than approximating it with formatting hacks — a document that merely looks right (e.g., colored/struck-through text standing in for <w:ins>/<w:del>) will fail any real redline/legal review because it carries no revision metadata.

Default Workflow

  1. Identify the file type and trust level: .docx/.dotx vs .docm/.dotm vs legacy .doc.
  2. Pick the lane:
    • Template generation -> docxtpl
    • Programmatic structure edits -> python-docx or docx
    • Review/comments -> comments or Word Compare
    • LLM extraction -> MarkItDown, Docling, or mammoth
  3. Generate or modify the document.
  4. Run scripts/docx_quality_gate.py and, when needed, scripts/docx_inspect_ooxml.py.
  5. If shipping externally, validate rendering in Word plus at least one secondary viewer and apply accessibility hygiene.

Core Operations

Create A Document (Python - python-docx)

python
from docx import Document
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.shared import Inches

doc = Document()

title = doc.add_heading("Quarterly Review", 0)
title.alignment = WD_ALIGN_PARAGRAPH.CENTER

doc.add_paragraph("Executive summary goes here.")

table = doc.add_table(rows=2, cols=2)
table.style = "Table Grid"
table.rows[0].cells[0].text = "Metric"
table.rows[0].cells[1].text = "Value"
table.rows[1].cells[0].text = "Revenue"
table.rows[1].cells[1].text = "$1.2M"

doc.add_picture("chart.png", width=Inches(4.5))
doc.save("quarterly-review.docx")

Add A Review Comment (Python - python-docx)

python
from docx import Document

doc = Document()
paragraph = doc.add_paragraph("This clause needs legal review.")

comment = doc.add_comment(
    runs=paragraph.runs,
    text="Clarify whether this applies to renewals as well.",
    author="Legal",
    initials="LG",
)

comment.paragraphs[0].add_run(" Add the renewal edge case explicitly.").bold = True
doc.save("reviewable.docx")

Fill A Template (Python - docxtpl)

python
from docxtpl import DocxTemplate

doc = DocxTemplate("template.docx")
context = {
    "company_name": "Acme Corp",
    "contract_date": "2026-03-13",
    "items": [
        {"name": "Widget A", "price": 100},
        {"name": "Widget B", "price": 200},
    ],
}
doc.render(context)
doc.save("filled-template.docx")

Convert Trusted DOCX To HTML (Script)

bash
node scripts/docx_to_html.mjs input.docx output.html --style-map custom-style-map.txt --extract-images-dir output-assets/

Extract Structure For Automation (Script)

bash
python3 scripts/docx_extract.py input.docx --include headers footers hyperlinks comments images --out extracted.json

Output Quality Checklist

  • Structure: heading hierarchy, list styles, and tables are intentional and consistent.
  • Reviewability: comments or Word Compare are used for feedback-heavy workflows instead of ad hoc formatting hacks.
  • Safety: macro-enabled files are treated as untrusted; HTML generated from DOCX is sanitized before use.
  • Portability: fonts, numbering, tables, and images are checked in at least one non-Word viewer when documents are distributed.
  • Accessibility hygiene: headings, descriptive links, table headers, document language, and alt text are present where needed.
  • Release quality: run scripts/docx_quality_gate.py before shipping or batch-publishing.

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

  • Convert trusted DOCX content into Markdown/HTML/JSON for search or RAG.
  • Summarize meeting notes into a Word template, but keep humans accountable for factual accuracy.
  • Generate first-pass reports/contracts from structured data, then route through human review.

Navigation

Resources

Scripts

  • scripts/docx_inspect_ooxml.py - Dependency-free OOXML inspection for tracked changes and comments
  • scripts/docx_extract.py - Extract text, tables, metadata, and optional headers/footers/hyperlinks/comments/images to JSON
  • scripts/docx_render_template.py - Render a docxtpl template from JSON
  • scripts/docx_to_html.mjs - Convert trusted .docx to HTML with style maps and optional image extraction
  • scripts/docx_quality_gate.py - Validate parseability, unresolved template tags, tracked-change/comment signals, and optional LibreOffice conversion

Templates

Related Skills

Fact-Checking

  • Use data/sources.json as the starting set of primary sources.
  • Use web search/web fetch to verify current external facts, versions, release behavior, regulations, and platform quirks before final answers.
  • Prefer primary documentation, package pages, release pages, and official standards pages.
  • If web access is unavailable, state the limitation and mark volatile guidance as unverified.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.

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 Document Docx AI skill do?

Create/edit .docx files with styles, templates, comments, and extraction workflows. Use when asked to generate Word reports, contracts, proposals, or convert Word content.

Why use Document Docx on TypingMind?

Because you install it once and use it with any model. Document Docx 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 Document Docx in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vasilyu1983/AI-Agents-public/tree/main/frameworks/shared-skills/skills/document-docx. 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 Document Docx?

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 Document Docx?

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

Is the Document Docx AI skill free?

Yes. It is published on GitHub by vasilyu1983 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇