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Humanize English

Community
AnastasiyaW
humanize-english

Edit English drafts for clear, natural language while preserving facts, uncertainty, technical meaning, citations and genre. Use when: humanize this text, remove AI-style filler, improve English prose, or edit an English article before publication. Not an authorship detector or a promise to bypass one. For Russian use humanize-russian; for thesis/evidence and structure use article-structure-review.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill namehumanize-english
Stars
212
Forks
32
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Humanize English 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/writing/humanize-english
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/writing/humanize-english .claude/skills/humanize-english
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Humanize English 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 Humanize English 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 Humanize English 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.

Clear, Natural English Editing

Improve the reader's understanding, not a detector score. A good reference can have repeated terminology, regular tables, definitions and no anecdote. Keep the user's intended voice and the destination's format.

Facts Are Not Style Material

  • Preserve supplied numbers, units, dates, versions, names, citations, negation, uncertainty and claim scope. Do not silently update a historical date.
  • Never invent a count, quote, interview, personal experience, experiment, failure, comparison or source to make prose more vivid.
  • Add specificity only when the supporting evidence supplies it. If no measurement exists, retain uncertainty or remove an unsupported claim. An unsupplied number is not an improvement over a vague quantity.
  • Label illustrative scenarios as hypothetical. Do not present them as a real case study or treat their invented values as proof of a product claim.
  • Preserve identifiers, executable code and technical obligations. Words such as may, must, can and will are not interchangeable style alternatives.
  • Reuse provided facts without asking the user to repeat them. Research a material factual gap when research is in scope; otherwise identify the gap without manufacturing an answer or blocking supported edits.

Match the Genre

GenreUseful editingAvoid forcing
technical referenceexact terms, clear conditions, scannable structureslang, story openings, rhetorical questions, sales CTA
explanation/tutorialprerequisites and action/result sequencean untested command described as verified
analysisseparate observation, inference and limitsstronger causal claims than the evidence supports
personal narrativethe author's supplied experiences and voiceinvented first-person testimony
marketinga supported benefit and relevant next actionfabricated testimonials, percentages or guarantees

Contractions, fragments, humour, analogies and direct address are options, not quotas. Use them when they fit the audience and improve comprehension. Parallel lists and consistent terminology can be desirable in documentation.

Editing Procedure

  1. Identify the reader's task, genre, source material and protected facts from the existing request and draft. Do not add an unnecessary intake questionnaire.
  2. Remove empty intensifiers and duplicated ideas. Use direct verbs when meaning is preserved; keep connectors that express real causality or contrast.
  3. Clarify who acts, on what, under which conditions and with what result. Do not supply a result that the source does not establish.
  4. Fix awkward rhythm where it hinders reading. Do not maximize variation, enforce a paragraph pattern or replace terms to appear less predictable.
  5. Compare input and output for semantic changes. Check quantities with their units, denominator, time frame and uncertainty, not just the numeral.
  6. Return the edited text and any necessary factual caveat. Once the edit is sufficient, continue the remaining requested publication workflow rather than starting another stylistic pass automatically.

Examples: Same Facts, Clearer Prose

Supplied textFaithful edit
In order to load the file, the user must select Open.To load the file, select Open.
The tool may reduce processing time; no benchmark is available.The tool may save processing time, but no benchmark is available.
Some testers reported failures. The sample size was not recorded.Some testers reported failures; the sample size was not recorded.
On 2026-09-01, 12 of 40 runs failed with version 2.1.With version 2.1, 12 of 40 runs failed on 2026-09-01.

These are editing fixtures, not results from a deployed product. Unknown sample size stays unknown; a date, denominator and version do not disappear for fluency.

Verification

  • Every changed factual assertion still maps to the supplied material or a source actually checked for this task.
  • Conditions, uncertainty, negatives, numbers, units, dates, versions and code are preserved unless an evidence-backed factual correction was requested.
  • The result helps its intended reader without an unrequested genre change.
  • No invented testimony or detector-evasion claim was added.

A no-change verdict is valid for an already clear passage. Do not add numbers, questions, opinions, deliberate errors or fake admissions to satisfy a score.

Gotchas

  • Detector score as evidence: it establishes neither authorship nor factual accuracy. Do not promise an undetectable edit.
  • Replacing every vague quantity: guessing a plausible percentage fabricates evidence when the sample size is unknown.
  • Forced informality: slang can make a reference harder to understand, particularly for readers using English as an additional language.
  • Synonym churn: repeating a technical term is safer than creating a false distinction through alternate names.
  • Example contamination: a skill example is not an event that happened to the user or author.

Troubleshooting

SymptomCheckCorrection
polished but unsupported claimsource scope and conditionsrestore supported meaning or remove the claim
bureaucratic phrasingaction and actoruse a direct sentence without changing obligations
repetitive textrepeated meaning versus necessary termscut redundancy, keep precise terminology
reference sounds like an advertisementrequested genreremove promotion and invented narrative
detector flags the passageactual writing and detector limitationsrepair evidenced defects, not a guessed authorship score

Sources and Maintenance

Reviewed 2026-09-06. Owner: the existing writing-skill maintainers. Change through canonical Git, focused before/after fixtures, independent review and the supported installer; retain the prior revision and installation backup for rollback. Refresh sources when guidance changes or a factual/genre regression is observed, not by automatically rewriting working prose on a schedule.

Frequently asked questions

What does the Humanize English AI skill do?

Edit English drafts for clear, natural language while preserving facts, uncertainty, technical meaning, citations and genre. Use when: humanize this text, remove AI-style filler, improve English prose, or edit an English article before publication. Not an authorship detector or a promise to bypass one. For Russian use humanize-russian; for thesis/evidence and structure use article-structure-review.

Why use Humanize English on TypingMind?

Because you install it once and use it with any model. Humanize English 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 Humanize English in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/writing/humanize-english. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Humanize English?

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 Humanize English?

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

Is the Humanize English AI skill free?

Yes. It is published on GitHub by AnastasiyaW 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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