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Chinese Thesis Workbench

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jsChen-biostat
chinese-thesis-workbench

Standardize, draft, revise, check, and package Chinese undergraduate thesis or graduation-design papers from school templates, task books, proposals, sample papers, source code, screenshots, databases, APIs, tests, literature PDFs, Word comments, and existing drafts. Use when the user asks to write, generate, refactor, polish, reduce AIGC style, verify, format, or deliver a Chinese thesis with evidence traceability, figure registries, workflow logs, chapter word control, screenshots, references, DOCX output, and appendix DOCX.

Overview

PublisherjsChen-biostat
Repositorymath-modelCN
Skill namechinese-thesis-workbench
Stars
59
Forks
0
Bundled files
68
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.

  • 68 bundled files

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

  • Open source

    Published by jsChen-biostat on GitHub. Read the source before you install it.

Installation

Install the Chinese Thesis Workbench 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/jsChen-biostat/math-modelCN/tree/main/04_chinese-thesis-workbench__chinese-thesis-workbench/chinese-thesis-workbench-skill-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/jsChen-biostat/math-modelCN.git /tmp/math-modelCN
mkdir -p .claude/skills
cp -r /tmp/math-modelCN/04_chinese-thesis-workbench__chinese-thesis-workbench/chinese-thesis-workbench-skill-main .claude/skills/chinese-thesis-workbench
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chinese Thesis Workbench 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 Chinese Thesis Workbench 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 Chinese Thesis Workbench 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.

Chinese Thesis Workbench

Operating Model

Use standards and evidence as the control layer, then use the Chinese thesis drafting pipeline as the output layer. Treat school templates, advisor requirements, task books, sample papers, source code, databases, APIs, tests, literature PDFs, screenshots, Word comments, and existing drafts as materials that must be resolved into structured state before formal prose is written.

The workbench has two sides:

  • Governance side: paper-context/ stores workflow state, standards, evidence, literature, AIGC reports, and Word comment revisions.
  • Delivery side: paper-output/ stores thesis Markdown, DOCX, appendix DOCX, figures, screenshots, image maps, and reference verification artifacts.
  • User-facing visibility and decision side: paper-context/workflow/user-dashboard.md summarizes current progress, missing materials, user decisions needed, and the next recommended action. paper-context/workflow/content-decisions.md records optional content emphasis and exclusion decisions when candidate content is available. paper-context/workflow/blocker-report.md records the latest blocker, options, recommendation, and limited-continuation status. paper-context/workflow/user-decisions.md records user-approved choices that affect scope, evidence, standards, outline, delivery, or limitations. These files improve transparency; they do not override standards, evidence, or delivery quality gates.

Required Workflow

Run the workflow in this order unless the user is only asking for a narrow inspection or revision:

  1. intake_materials: collect materials and record priority, gaps, missing impact, continuation limits, and user next steps in paper-context/workflow/material-inventory.md.
  2. init_workspace: initialize thesis-ai-standard/, paper-context/, and workflow logs.
  3. resolve_standards: fill thesis-ai-standard/templates/standard-profile.yaml.
  4. analyze_sample_and_template: normalize school template and sample-paper parser outputs into lightweight analysis reports, outline suggestions, and word budgets before drafting; parser output does not drive DOCX formatting unless the user explicitly selects sample-style generation via --sample-analysis or template-copy filling via --from-template.
  5. build_evidence: extract project facts into paper-context/evidence/.
  6. stop_and_report: stop or limit only the affected scope whenever evidence, standards, figures, citations, or DOCX delivery cannot be verified; record options in blocker-report.md.
  7. build_thesis_spec: fill thesis-ai-standard/templates/thesis-ai-spec.yaml.
  8. build_figure_registry: fill thesis-ai-standard/templates/figure-registry.yaml.
  9. confirm_outline: confirm chapter structure, word counts, sample/template observations, and any available content emphasis/exclusion decisions before writing.
  10. draft_chapters: write only from confirmed structured facts and evidence.
  11. produce_assets: generate or collect figures, diagrams, screenshots, tables, and appendix sources.
  12. produce_docx: generate or edit the main DOCX and appendix DOCX into paper-output/ using the recorded delivery path.
  13. quality_gates: run standards, evidence, reference, figure, DOCX, and AIGC checks.
  14. delivery_report: report outputs, limitations, remaining human decisions, and verification evidence.

stop_and_report is a global blocking mechanism, not just step 6. Read references/workflow/stop-and-report.md whenever continuing may require guessing.

Phase + Status State Model

Use this two-layer state model in paper-context/workflow/workflow-status.md, and mirror the user-facing summary in paper-context/workflow/user-dashboard.md:

FieldAllowed values
phaseintake_only, workspace_ready, standards_resolved, sample_analysis_done, evidence_built, spec_confirmed, outline_confirmed, writing_allowed, delivery_done
statuspending, in_progress, blocked, needs_review, done, deprecated

When status becomes blocked, write blocked_reason, missing_materials, next_action, and can_continue_with_limitations in the status file. Do not hide blockers in chat only.

After any meaningful phase, blocker, material, outline, or delivery-scope change, update user-dashboard.md so the user can see:

  • current phase and status
  • completed work
  • decisions waiting for user confirmation
  • missing materials and their impact
  • next recommended action
  • limited-continuation options, if any

Decision Tree

  1. If there is no workspace, run scripts/workspace/init_thesis_workspace.py.
  2. If school/advisor/template rules are missing or conflicting, resolve standards first.
  3. If sample/template analysis is missing, analyze it before thesis-ai-spec.yaml.
  4. If source evidence or literature is missing, build evidence and reference pools before writing.
  5. If thesis-ai-spec.yaml is not confirmed, do not write formal thesis body.
  6. If figure-registry.yaml is not ready, do not claim figures/tables/screenshots are complete.
  7. If asked to revise Word comments, extract comments and route back to writing_allowed.
  8. If asked for final delivery, run quality gates and produce both main and appendix DOCX.

Hard Rules

  • School and advisor requirements override default rules.
  • Do not write formal thesis body before materials are collected or the user explicitly confirms there are no more materials.
  • During intake, classify materials as required, strongly_recommended, or optional, and explain the effect of each missing required or strongly recommended material.
  • Use content-decisions.md when the user provides feature/module/experiment/appendix candidates, but do not block the main workflow only because no candidate content has been provided yet.
  • Record user-approved scope, material-unavailable, limited-continuation, standard-conflict, content-exclusion, outline, word-count, DOCX, appendix, and filename decisions in user-decisions.md.
  • User-facing workflow files are decision aids only. They must not weaken school/advisor requirements, evidence requirements, citation verification, figure provenance, or DOCX delivery checks.
  • Do not write formal thesis body before standards, sample/template analysis, and evidence building are complete.
  • When evidence is insufficient, trigger stop_and_report; do not guess missing facts.
  • When blocked, classify the issue as hard_blocker, limited_continue, or user_choice_needed, then provide user options and a recommended path.
  • thesis-ai-spec.yaml is the single entry point for thesis facts.
  • figure-registry.yaml is the single entry point for figures, tables, screenshots, and diagram sources.
  • Thesis prose may consume structured facts and evidence only; do not expand directly from README files or old notes.
  • Do not write content that content-decisions.md marks as rejected, excluded, or waiting for evidence.
  • Do not invent features, fields, APIs, test results, experiment data, or references.
  • AIGC style governance runs after the evidence chain is complete and may only improve academic expression, evidence density, and vague wording.
  • Thesis body must not expose AI workflow language.
  • Chapter 4 implementation must bind to real modules, screenshots, core code, SQL, or equivalent evidence.
  • System-design theses without an E-R diagram or equivalent data-design evidence cannot be marked complete.
  • Generate both the main thesis DOCX and appendix DOCX. For strict school formatting, prefer template-copy filling via scripts/docx/apply_textual_edits.py --from-template; otherwise use default or sample-style generation. Do not promise full template reproduction. Preserve diagram source, E-R source, flowchart source, and related assets in the appendix.
  • Formula delivery must be explicit: latex_text preserves source formula text, while formula_image requires matching image assets.
  • Output filenames must use the thesis title, not generic names such as final, draft, paper-final, or doc1.
  • Literature workflow must be: build pool -> verify -> filter -> format -> generate verification checklist.

Resource Map

NeedResource
Intake and fast workflowreferences/workflow/intake.md, references/workflow/rapid-thesis-workflow.md
Workflow state, blockers, and sparse-material handoffreferences/workflow/workflow-state-management.md, references/workflow/stop-and-report.md, references/workflow/material-gap-handoff.md
Standards and template resolutionreferences/standards/standards-and-template-resolution.md, references/standards/style-extraction.md, references/standards/default-style.md
Evidence extractionreferences/evidence/source-to-thesis-workflow.md, references/evidence/fact-extraction.md
Literature/PDF workflowreferences/evidence/literature-and-pdf-workflow.md, scripts/literature/
Writing and chapter controlreferences/writing/writing-pipeline.md, references/writing/chapter-patterns.md, references/writing/sample-analysis.md
AIGC style governancereferences/writing/aigc-style-governance.md, scripts/review/analyze_aigc_style.py
DOCX delivery and Word commentsreferences/delivery/docx-delivery.md, references/delivery/word-comment-revision-workflow.md, scripts/docx/
Figures and screenshotsscripts/figures/, scripts/screenshots/, thesis-ai-standard/templates/figure-registry.yaml
Merge provenancereferences/merge-map.md

Quality Gates

Before claiming delivery quality, check:

  • standard-profile.yaml, thesis-ai-spec.yaml, and figure-registry.yaml exist and are internally consistent.
  • Every major factual claim has a source in paper-context/evidence/ or verified literature.
  • Every figure/table has source_kind, source_path, image_path, evidence_source, first_mention, and status.
  • References are real, selected from a verified pool, and have a generated verification checklist.
  • AIGC style changes do not add facts.
  • Main DOCX and appendix DOCX exist in paper-output/.
  • Screenshot, diagram, and image-map paths resolve on Windows.
  • Workflow logs reflect the current phase, status, blockers, and delivery limitations.
  • user-dashboard.md reflects the user-visible progress, pending decisions, missing materials, and next action.
  • blocker-report.md reflects the latest blocker, affected scope, user options, recommended path, and whether limited continuation is allowed.
  • user-decisions.md records user-approved choices that affect scope, missing-material handling, limitations, standards, outline, or delivery.
  • If content-decisions.md has active candidates, their approved/deferred/excluded status is consistent with the outline, thesis spec, and figure registry.

Delivery Contract

Deliver thesis artifacts under paper-output/:

  • <论文标题>.md
  • <论文标题>.docx
  • <论文标题>-附件.docx
  • <论文标题>-image-map.json
  • <论文标题>-文献核验清单.json
  • figures/
  • screenshots/

Report what was verified, what could not be verified, and what still needs human confirmation. If verification cannot run, state the command and the reason.

Bundled files

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

and 8 more files.

Frequently asked questions

What does the Chinese Thesis Workbench AI skill do?

Standardize, draft, revise, check, and package Chinese undergraduate thesis or graduation-design papers from school templates, task books, proposals, sample papers, source code, screenshots, databases, APIs, tests, literature PDFs, Word comments, and existing drafts. Use when the user asks to write, generate, refactor, polish, reduce AIGC style, verify, format, or deliver a Chinese thesis with evidence traceability, figure registries, workflow logs, chapter word control, screenshots, references, DOCX output, and appendix DOCX.

Why use Chinese Thesis Workbench on TypingMind?

Because you install it once and use it with any model. Chinese Thesis Workbench 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 Chinese Thesis Workbench in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jsChen-biostat/math-modelCN/tree/main/04_chinese-thesis-workbench__chinese-thesis-workbench/chinese-thesis-workbench-skill-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 Chinese Thesis Workbench?

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 Chinese Thesis Workbench?

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

Is the Chinese Thesis Workbench AI skill free?

Yes. It is published on GitHub by jsChen-biostat 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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