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Latex To Word Workflow

Community
hajimi-kun
latex-to-word-workflow

Load when the user asks to convert an academic LaTeX manuscript, thesis, or report into an editable Word DOCX, adapt it to a Word template, preserve citations or cross-references, or diagnose a LaTeX-to-Word conversion problem. Do not use for PDF-only LaTeX compilation, general Word editing, or DOCX-to-LaTeX conversion.

Overview

Publisherhajimi-kun
Repositorylatex-to-word-workflow
Skill namelatex-to-word-workflow
Stars
134
Forks
8
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

    Published by hajimi-kun on GitHub. Read the source before you install it.

Installation

Install the Latex To Word Workflow 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/hajimi-kun/latex-to-word-workflow/tree/main/latex-to-word-workflow
  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/hajimi-kun/latex-to-word-workflow.git /tmp/latex-to-word-workflow
mkdir -p .claude/skills
cp -r /tmp/latex-to-word-workflow/latex-to-word-workflow .claude/skills/latex-to-word-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Latex To Word Workflow 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 Latex To Word Workflow 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 Latex To Word Workflow 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.

LaTeX to Word

Produce a submission-grade Word candidate from a complete LaTeX project. The candidate must preserve scientific meaning, use the confirmed target semantics, remain editable where practical, and include an explicit status for the user-run Word/Zotero acceptance steps.

Non-negotiable defaults

  • Preserve the original LaTeX, bibliography, figures, and authoritative Word target. Work only on generated files or explicit copies.
  • Use Pandoc as the initial editable-structure converter. Add source annotations, Lua, OpenXML, or supported-adapter Word automation only for an observed bridge mismatch.
  • For a new project, initialize examples/bridge-starter/ into the generated build directory. Reuse docx_bridge.py for fields, bookmarks, styles, and DOCX packaging; adapt configuration or project_adapter.py instead of reimplementing those primitives.
  • For every new or changed target, distill the required roles (not unrelated template styles) and pass a representative probe before full conversion.
  • Treat object packaging and layout as target-owned contract values. Paragraphs, tab stops, borderless tables, caption segment sequences, label literals, and object order are possible representations, not generic skill defaults.
  • When citations exist, Zotero-live fields are mandatory. Run the fixed Zotero-live setup and probe in references/conversion-playbook.md#zotero-live-citations; repair agent-side configuration where possible and give the user exact manual steps for the remaining Word/Zotero setup.
  • Convert every LaTeX cross-reference occurrence to a verified native Word field by default. Static references require an informed, recorded user opt-out.
  • Treat desktop Microsoft Word as the authoritative environment for fields, numbering, layout behavior, and final validation. Word and Zotero actions are user-run acceptance steps unless the host explicitly provides a supported desktop adapter; never assume GUI, COM, or process access.
  • Prioritize: scientific fidelity, content completeness, citation/numbering semantics, editability, template structure, visual fidelity, then automation cost.

Capability boundary

  • Required for execution: access to the project files and a viable DOCX conversion path. If either is absent, provide diagnosis or a handoff plan and report the conversion as blocked.
  • Optional capabilities include shell or Python execution, LaTeX compilation, local application/process detection, Word rendering, GUI automation, and visual inspection. Attempt one only when the host explicitly exposes it.
  • An unavailable optional capability becomes a user-run gate, not a failed conversion. An unobservable capability remains unknown; never convert unknown into an installation or compatibility claim.

Read routing

SituationRequired read
New, changed, example-only, or ambiguous Word targetreferences/template-distillation.md, then references/conversion-playbook.md#build-the-semantic-bridge
First build for a projectexamples/bridge-starter/, then references/conversion-playbook.md#build-the-semantic-bridge
Zotero, cross-references, equations, captions, complex objects, or diagnosisRelevant section of references/conversion-playbook.md
Candidate validation or handoffreferences/conversion-playbook.md#validate-and-hand-off
Other conversion workScan this file, then load only the relevant playbook section

Required workflow

  1. Read the user request and nearest project instructions. Identify the complete LaTeX entry point, authoritative Word target, output path, and capabilities exposed by the host.
  2. Compile the complete LaTeX project when the host exposes the toolchain; otherwise request the user's compiled reference or record compilation as a pending evidence gate. Inventory included sources, semantic roles, figures, tables, equations, citations, labels, reference occurrences, appendices, custom macros, and numbering behavior.
  3. If citations exist, complete the mandatory Zotero setup and probe before expensive manuscript post-processing. Never use the absence of a zotero CLI alone as evidence that Zotero is unavailable. If live fields cannot be generated, stop the citation-bearing build and hand off the exact setup gate.
  4. Classify and distill every new or changed Word target into an explicit role contract. Do not treat --reference-doc as an executable specification.
  5. For a new project, run python scripts/init_bridge.py <build-dir> and fill bridge_config.json from the role contract. Keep stable field, bookmark, and package operations in the copied docx_bridge.py; put only project-specific behavior in project_adapter.py or the semantic Lua filter.
  6. Build the bridge LaTeX role -> Pandoc output -> target Word representation -> method. Resolve every required role before full conversion.
  7. Convert a representative probe covering every high-risk role and transition. Inspect actual styles, formatting, fields, bookmarks, lists, and objects.
  8. Correct each mismatch at its responsible layer. Start the complete conversion only after the probe passes the applicable contract rows.
  9. Build the complete candidate and validate source, target-contract, and DOCX evidence. Counts are diagnostic; semantic correspondence is the gate.
  10. Follow references/conversion-playbook.md#validate-and-hand-off. Give the user a disposable candidate and exact Word/Zotero acceptance steps. If the user returns the refreshed file, re-run the audit and compare pre/post evidence.
  11. Deliver the candidate, agent-side evidence, user-refresh status, adaptations, unresolved items, and approved exceptions.

Bundled tools

Use the bundled standard-library scripts when Python 3 is available. init_bridge.py creates generated bridge files only; the manifest and audit scripts are read-only over source and DOCX inputs.

text
python scripts/latex_manifest.py <main.tex> --output <source-manifest.json>
python scripts/init_bridge.py <build-dir>
python scripts/docx_audit.py <candidate.docx> \
  --latex-manifest <source-manifest.json> \
  --require-native-crossrefs --require-zotero-fields \
  --output <candidate-audit.json>
python scripts/docx_audit.py <refreshed.docx> \
  --latex-manifest <source-manifest.json> \
  --baseline <candidate-audit.json> \
  --require-native-crossrefs --require-zotero-fields

--require-zotero-fields checks generated field presence, not that Refresh has occurred. Scripts provide package and semantic evidence; they cannot replace user-run Word rendering, field refresh, Zotero refresh, or user confirmation.

Stop conditions

Do not label the candidate fully verified when a required role is unmapped, a native field lacks a valid target, a REF target contains an entire drawing or table, the user reports a Word repair warning, live field generation is unverified, the user refresh/save/reopen gate is pending, or an unresolved scientific-content mismatch remains. A pending user gate may be delivered as a clearly labelled candidate; never silently downgrade it.

Known gotchas

  • Imported styles are not evidence that generated content uses them correctly. See references/template-distillation.md#build-the-role-contract.
  • ensuremath can turn prose formulae and units into unwanted OMML. See references/conversion-playbook.md#preserve-text-and-math-semantics.
  • A Pandoc bookmark can span a whole object, causing REF to reproduce it. See references/conversion-playbook.md#native-word-cross-references.
  • Raw OpenXML and field construction are starter-engine responsibilities, not project-adapter code. See examples/bridge-starter/.
  • Word/Zotero refresh can change fields and document structure. See references/conversion-playbook.md#validate-and-hand-off.
  • Zotero Refresh updates existing Zotero fields; it cannot create fields from non-Zotero citation text. See references/conversion-playbook.md#zotero-live-citations.
  • Field update can discard caption-run formatting or expose faulty equation packaging or alignment. See references/conversion-playbook.md#word-specific-objects.
  • Equation container (e.g. 1×3 borderless table) is not the same as column widths. Short template demos often use equal thirds; long OMML with fractions or \qquad secondaries needs a wide math cell or Word reflows mid-expression. Probe with a complex display equation. See references/conversion-playbook.md#numbered-equations.
  • PDF, EPS, TikZ, and PGFPlots artwork needs explicit Word-supported output. See references/conversion-playbook.md#high-risk-objects.

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 Latex To Word Workflow AI skill do?

Load when the user asks to convert an academic LaTeX manuscript, thesis, or report into an editable Word DOCX, adapt it to a Word template, preserve citations or cross-references, or diagnose a LaTeX-to-Word conversion problem. Do not use for PDF-only LaTeX compilation, general Word editing, or DOCX-to-LaTeX conversion.

Why use Latex To Word Workflow on TypingMind?

Because you install it once and use it with any model. Latex To Word Workflow 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 Latex To Word Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hajimi-kun/latex-to-word-workflow/tree/main/latex-to-word-workflow. 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 Latex To Word Workflow?

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 Latex To Word Workflow?

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

Is the Latex To Word Workflow AI skill free?

Yes. It is published on GitHub by hajimi-kun 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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