Writing DNA Distiller

中文 | English

Skill name: writing-dna-skill

An agent skill for distilling the writing style of any author or publication into reusable rules.

This is not a simple "write like this person" prompt. It gives an agent a complete distillation workflow for turning a corpus of full articles into reusable Writing DNA: language, structure, topic selection, source strategy, cognitive frames, and visual style. Before writing, the agent can reread these documents to reproduce the target style with greater consistency.

The Problem It Solves

When people ask AI to imitate an author, they often provide a few examples and say, "Write in this style." The result is unreliable because the agent is guessing from surface-level tone.

writing-dna-skill takes a different approach. Instead of asking the agent to imitate immediately, it first analyzes a substantial corpus of complete articles, turns recurring style choices into explicit rules, and then uses those rules to guide future writing.

The skill examines:

• recurring words, sentence structures, punctuation, and rhythm
• how articles open, develop, transition, and close
• which topics the author selects or avoids
• which sources, examples, data, and screenshots appear repeatedly
• the worldview, value judgments, and assumptions behind the writing
• image strategy, layout, typography hierarchy, and color when the source is visual

Best For

• analyzing the stable writing style of an author, account, or publication
• turning your own writing history into a reusable style asset
• comparing how different authors approach the same topic
• giving an AI agent more reliable context for style-consistent writing

Chinese And English Artifacts

The skill keeps the conversation language separate from the artifact language. A user may speak Chinese while analyzing an English newsletter, or speak English while building a Chinese writing system.

• Chinese artifacts use templates/author-corpus/zh/ and Chinese filenames.
• English artifacts use templates/author-corpus/en/ and English filenames.
• An explicit user choice takes priority. Otherwise, the source corpus determines the artifact language.

What It Can Analyze

The skill turns "style" from a vague impression into rules that can be recorded, reused, and checked. It looks beyond tone to understand why an article is written in a particular way, how evidence is organized, and how conclusions are formed.

Suitable source material includes:

• long-form articles, newsletters, columns, blogs, and public-account posts
• image-led posts on Xiaohongshu, Weibo, and other social platforms
• your own archive of past writing
• articles by multiple authors writing about the same subject

If the source includes screenshots, illustrations, charts, or distinctive formatting, preserve them. For visual publications, image placement, evidentiary screenshots, heading hierarchy, and whitespace can be as important as the prose itself.

Six Layers of Distillation

Layer	What It Analyzes	Output
L1 Surface Language	Word frequency, sentence length, punctuation, rhetoric, recurring expressions	language-dna.md
L2 Article Structure	Opening hook, body architecture, transitions, closing pattern	structure-patterns.md
L3 Topic Logic	Timing, angle selection, topic priorities	cognitive-framework.md
L4 Source Strategy	References, authorities, examples, and use of data	cognitive-framework.md
L5 Cognitive Frame	Worldview, values, assumptions, and recurring propositions	cognitive-framework.md
L6 Visual Style	Image strategy, formatting, typography hierarchy, and color	visual-style-guide.md

In short:

• L1-L2 explain how the author writes.
• L3-L5 explain how the author thinks.
• L6 explains how the work is presented.

Reliable style reproduction needs all three. Language alone may sound similar while expressing different ideas. Structure alone may resemble the original outline but miss the voice. Ignoring visual style can make image-led content feel wrong even when the prose is close.

Corpus Requirements

For a real distillation, prepare:

• at least 20 complete articles
• .md or .txt source files
• material from different periods, topics, and article types when possible

examples/format-only/ demonstrates the expected directory structure. It is not a reliable style corpus, and a few short samples should not be used to judge the skill's full capability.

Distillation Workflow

A complete distillation normally follows six steps:

1. Collect the source corpus: Gather at least 20 complete articles and place them in raw/ or raw-corpus/.
2. Build metadata: Record the title, date, article type, topic, hook, structure, and source strategy for each article in _meta/.
3. Analyze language DNA: Measure word frequency, sentence length, punctuation, paragraph rhythm, recurring expressions, and mixed-language habits. Write language-dna.md.
4. Extract structure patterns: Mark openings, turns, body organization, and endings, then turn repeated article shapes into structure-patterns.md.
5. Identify cognitive frames: Distill topic logic, source preferences, values, assumptions, and recurring propositions into cognitive-framework.md.
6. Document visual style: When the corpus includes images or distinctive formatting, analyze image roles, screenshot evidence, typography, color, and text-image collaboration. Write visual-style-guide.md.

The agent then combines the results into Writing-DNA.md, the main document to reread before writing in the distilled style.

Output

A complete project usually produces:

author-or-publication/
├── raw/                    # Complete source articles
├── _meta/                  # Article-level metadata
├── language-dna.md          # L1 analysis
├── structure-patterns.md    # L2 analysis
├── cognitive-framework.md   # L3-L5 analysis
├── visual-style-guide.md    # L6 analysis
└── Writing-DNA.md          # Integrated style document

Writing-DNA.md is the primary reusable context for future writing sessions.

Quick Start

1. Collect or ask an agent to collect at least 20 complete articles and place them in raw/ or raw-corpus/.
2. Ask the agent to use writing-dna-skill, or to read SKILL.md and references/workflow.en.md, before processing the corpus.
3. Wait for the agent to produce:
   • language-dna.md
   • structure-patterns.md
   • cognitive-framework.md
   • visual-style-guide.md
   • Writing-DNA.md
4. Before future writing tasks, ask the agent to reread all distilled style documents.

Start a distillation with:

Use writing-dna-skill to distill the writing style from every article in this directory.

Writing With a Distilled Style

For more consistent results, do not load the style documents only once. Section six of SKILL.md makes the pre-writing reading a required step:

1. Give the agent the topic, audience, purpose, and relevant context.
2. Ask it to reread every distilled artifact — the four layered files plus Writing-DNA.md, skipping none. The integrated document holds compressed conclusions; the actual cadence lives in the layered files.
3. Then have it read 5 articles from raw/ that are closest in content type and subject. Filter on article_type and topic_tags in _meta/ first; when more than five match, take the most recent. Reading source articles is not about harvesting material — it calibrates what the artifacts cannot describe: how sentences breathe, how paragraphs connect, when a short sentence lands.
4. Require the draft to follow the language, structure, cognitive, and visual rules.
5. If the result does not feel accurate, ask the agent to audit the draft against language-dna.md, structure-patterns.md, and cognitive-framework.md, then rewrite it.

Priority when rules conflict: the user's explicit instructions for this piece > the structure pattern matching the content type > language characteristics and visual style > cognitive frames. Specific claims and facts from the source articles must not be carried into the new piece — you are reproducing how the author writes, not what they wrote.

Example:

Read every distilled style document in this author's directory, especially
Writing-DNA.md, language-dna.md, structure-patterns.md,
cognitive-framework.md, and visual-style-guide.md.

Then pick the 5 articles in raw/ closest to my subject, read them, and tell me
what they share in voice.

Finally, write an article for the target audience using those rules.
Topic: ...

After Writing: Removing AI Tone

However accurate the distillation, model output often still reads as machine-written. These are two separate problems. Distillation addresses does this sound like the author, while AI tone is does this sound like a machine. The style can be right while "it's not A, but B" keeps recurring, every subheading is numbered "one, two, three", and paragraphs open with a bare comment like "Sounds like...".

skills/lieflat-less-ai-tone/ is the final step in the workflow, clearing those tells against an explicit rule list. It installs together with this repository; no extra step is needed.

Then:

Apply the lieflat-less-ai-tone rules to the article you just wrote.
Read this author's language-dna.md first; where they conflict, the distilled
artifacts win.

It rewrites from a whitelist, touching only the listed problems; sentences matching no rule are preserved verbatim, and the article's structure is left intact. Every rule has a locatable trigger and was measured against 300 AI outputs and 329 human articles, rather than resting on "this reads like AI". Full methodology, data, and the fifteen hypotheses that failed are documented in lieflat-less-ai-tone.

Key point: where the distilled artifacts conflict with the AI-tone rules, the artifacts win. That is how the target author actually writes, not a machine tell. If an author genuinely favors em dashes, a generic rule should not strip them.

Why It Works

The skill separates writing style into layers that an agent can inspect, execute, and audit. Instead of relying on a vague impression of similarity, it records explicit style rules and directs the agent's attention back to them before each writing task.

The result is a reusable set of instructions rather than a one-time imitation prompt.

Important Boundaries

This project is intended for learning, analysis, style research, and building personal writing assets. Do not use it to impersonate an author, mislead readers, or violate copyright.

Do not place unauthorized source articles in a public repository. Public releases should contain directory templates, field schemas, documentation, and examples you have permission to share.

Repository Structure

writing-dna-skill/
├── SKILL.md
├── README.md
├── README.en.md
├── LICENSE
├── NOTICE
├── agents/
│   └── openai.yaml
├── references/
│   └── workflow.en.md
├── docs/
│   ├── release-checklist.md
│   └── usage-boundaries.md
├── templates/
│   └── author-corpus/
│       ├── zh/                  # Chinese templates and filenames
│       └── en/                  # English templates and filenames
├── skills/
│   └── lieflat-less-ai-tone/    # AI-tone rules (installed with this repo)
└── examples/
    └── format-only/

License

The current version is licensed under the PolyForm Noncommercial License 1.0.0 for noncommercial purposes only. Personal study, research, and other uses permitted by that license are allowed. Using this project in a commercial product, paid service, or for-profit organization's business, including embedding it in an agent product, requires a separate commercial license. Contact the repository maintainer for commercial licensing.

Copyright is not transferred by a usage license. Versions previously released under MIT remain available under their original terms; this change does not revoke rights already granted for those versions. See the MIT license at the commit before this change.