monograph-writing — Academic Monograph Batch-Writing Assistant

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Turn "writing a 12-chapter book" into an AI-assisted, script-validated, multi-author pipeline — with a hard no-fabrication guarantee for every citation.

Want to see it in action first? Browse the complete example project: examples/demo-project — every artifact from project initialization, section writing, and validation through stripped-clean-copy generation.

1. Who is this for?

Anyone writing a book-length manuscript: professors writing academic monographs, researchers writing technical books, engineers writing industry handbooks, graduate teams co-writing textbooks — especially if:

• the book has a dozen chapters and hundreds of thousands of words, and cannot be written in a single AI conversation;
• you use AI to write but fear it fabricating references and data;
• multiple people / multiple sessions work in relay, and you fear inconsistency and lost progress.

If any of these sound familiar, this skill was designed for you. No programming knowledge is required at any point — your AI assistant runs every script for you.

2. What problems does it solve?

3. Quick Start (3 steps)

Step 1: Install

If you use a command line (requires Node.js):

npx skills add newdeme/monograph-writing

No command line? Copy this sentence and paste it into your AI assistant — it will do everything:

Please install the monograph-writing skill from GitHub for me: clone the repository https://github.com/newdeme/monograph-writing, then copy only the three items the skill needs — SKILL.md, references/, and scripts/ — into a monograph-writing/ folder in your skill library (skip tests/, examples/, docs/ and .github/ — they are repository development files), confirm the skill is recognized, and tell me how to get started.

Works with any AI assistant that supports the Agent Skills open specification (Claude Code, Claude Agent SDK, Cherry Studio, …). Start a new conversation after installing.

Step 2: Prepare your table of contents

Put your book outline into one Markdown file (your AI can format it for you; see a complete example in examples/demo-project):

# Your Book Title — Table of Contents

## Chapter 1 Title

**1.1 Section Title**
- 1.1.1 Subsection Title
- 1.1.2 Subsection Title

**1.2 Section Title**
…

**1.N Summary**

Step 3: Say one sentence to your AI assistant

"Initialize my monograph project with the monograph-writing skill; the outline is at xxx.md"

The AI runs init_project.py to scaffold the project, then confirms word-count tiers and your evidence-corpus setup with you. After that, every time you say "continue", it advances to the next batch according to the ledger. You get a report after each batch: files written, validation results, and a preview of the next batch.

4. Bring-Your-Own Evidence Corpus (signature feature)

The two biggest fears when writing with AI: fabricated references, and your materials being out of the AI's reach. This skill solves both: you put your literature into a corpus beforehand, and the AI only searches what you gave it, verifying every citation against the original before it enters the manuscript.

At initialization, the "corpus onboarding interview" offers four choices (upgradeable at any time):

The laziest path: send the files to your AI assistant and say "organize these into the project's 02_语料 folder, grouped by theme".

The boundary of your corpus = the boundary of what can be cited: every document you put in should be one you're willing to see in your reference list. Setup steps for each option and upgrade paths: references/evidence-corpus.md.

5. The four automation scripts (your AI runs them; humans can read them)

All project parameters live in one file — 00_管理文件/书稿配置.json. Change word-count targets or add exemption tiers there; the scripts never need editing.

Note: script output and generated ledger templates are currently in Chinese (functionality is language-independent; internationalization is on the roadmap).

6. Data privacy & technical facts

• Your data never leaves your computer: all four scripts run locally — no network, no telemetry, no data collection. Your manuscript, outline, and ledgers stay on your own disk. Literature search goes through your own AI assistant and knowledge base, under your control.
• Minimal dependencies: pure Python standard library (any Python 3 ≥ 3.9); the only extra dependency is python-docx for Word merging.
• Platform compatibility: follows the Agent Skills open specification; works with any compliant AI assistant.
• Reporting issues: script bugs and security concerns go to GitHub Issues.

7. Core design principles

1. The ledger is the state: progress, conventions, numbering pointers, verified references — all recorded in project files on disk, not in conversations. Switch sessions, co-authors, or computers without losing anything.
2. Batch-by-batch progress: one section directory = one batch (all subsections + a section summary). Small deliveries; validation must reach zero errors before a batch closes, so errors never accumulate into disasters.
3. Judgment separated from execution: the author is the academic authority (arguments, evidence selection, final calls); the AI is the writing partner and process executor — it never replaces the author's judgment and never fabricates evidence.
4. Layered summaries against forgetting: section summaries (600–800 words) → chapter summaries written only from the section summaries, avoiding the context loss of re-reading entire chapters.
5. Preparation kept with the prose: every subsection file carries its argument tree and evidence list (three-part structure) for auditability during review; stripped automatically at book assembly.

8. Project structure

your-book/
├── 00_管理文件/            outline, config, progress ledger, terminology ledger, instruction list
├── 01_书稿/第X章 章标题/Y.Z 节标题/   the whole manuscript (grows batch by batch)
├── 02_语料/                your evidence corpus (PDF/Word/Excel/Markdown — you decide)
├── 03_归档素材/            external drafts (go through the repair workflow; never used directly)
└── 04_剥离版书稿/          clean copies + merged Word manuscript (script-generated)

9. Multi-author collaboration

• Each co-author owns several chapters; everyone follows the same method (this skill is the standard).
• Handover: the progress ledger + a handover note travel with the workspace.
• When the corpus can't travel with the files, the incoming author uses only the ledger's "verified references cache" (§4 of the ledger); new references go through the corpus owner — better to cite less than to cite wrong.

10. FAQ

Q: I have a pile of references (mixed PDF/Word/Excel/Markdown). How do I let the AI use them? Simplest: put them all into the 02_语料/ folder created at initialization (a usage note is inside). Any format, your call on content. Then tell the AI: "the corpus is in 02_语料/ — search and verify against it when writing." For large collections or semantic search, upgrade to an AI knowledge base or Zotero (see Section 4). In every mode the AI cites only corpus materials, verified one by one against the originals.

Q: Will my manuscript be uploaded or collected? No. All four scripts run locally on your computer — no network, no telemetry, no data collection. Manuscripts and ledgers stay on your disk. Literature search goes through your own AI assistant and knowledge base (see Section 6).

Q: I can't use a command line at all. Can I still use this? Yes. Installation is one copy-pasted sentence to your AI assistant; after that, all scripts are run by the assistant — you just talk ("continue", "next chapter", "set chapter 2 to 1200–1800 words").

Q: Can I use a citation style other than GB/T 7714? Yes. GB/T 7714 (the Chinese national standard, sequential numbering) is the default. Change citation_style in 书稿配置.json and set check_citation_style to false (that check is tailored to GB/T 7714 type identifiers).

Q: Why is citation checking so strict? In real monograph writing, out-of-order citation numbers and reference lists that don't match the text are the most frequent errors — manually auditing a 300,000-word book is nearly impossible. Script-enforced checks on every batch are what make the final manuscript trustworthy.

Q: My platform isn't Claude — will it work? The skill follows the Agent Skills open specification (SKILL.md + scripts + reference docs), so any compliant AI assistant can use it. The scripts are pure Python standard library (except python-docx for Word merging) and can also be used standalone.

Q: What if I need to change the outline mid-writing? The frozen outline is a serious contract: changes require author approval and a version bump (v2, v3…), recorded in the ledger. Completed chapters are never silently rewritten — contradictions are logged for adjudication. See the references/ docs.

11. Citing this project

If this skill helps your research, teaching, or work, a citation is the best way to support it. Click the "Cite this repository" button on the repository homepage (driven by CITATION.cff) to get a ready-to-use citation, or use the BibTeX entry below:

@software{newdeme_monograph_writing,
  author  = {newdeme},
  title   = {monograph-writing: Academic Monograph Batch-Writing Assistant},
  year    = {2026},
  url     = {https://github.com/newdeme/monograph-writing},
  version = {1.2.1},
  license = {Apache-2.0}
}

12. Contributing & license

• This skill distills the complete writing practice of a real academic monograph — the methodology is battle-tested over 200+ section batches.
• Feedback and improvements welcome via Issues / Pull Requests (batch experiences, new validation rules, adaptations to other citation styles). For commercial cooperation, private-deployment guidance, or custom development, please reach the maintainer via GitHub Issues.
• Released under the Apache License 2.0, © 2026 newdeme — includes patent grant, safe for academic and commercial use.

Co-developed by an AI writing assistant and a monograph author. Methodology details: SKILL.md and the references/ directory.