You read a draft and understand every sentence, yet struggle to remember its point. There are promises of transformation, generous adjectives and a conclusion that could end almost any article. The editing task is to decide what deserves to stay.

Humanizer turns observations about AI-generated writing into editing instructions. To understand its value, start with its source: the Wikipedia guide that inspired the skill and the problem of AI slop. Then we will cover installation and revise an original example without losing an important condition.

Why Humanizer exists

Humanizer addresses an editing problem: generated text can be well formed yet generic. Instead of explaining what a tool does, it announces that the tool transforms an industry. Instead of identifying a source, it invokes “experts” nobody can look up. Readers get an impression of authority with little they can use.

The Humanizer repository↗ states its purpose: revise AI-sounding writing without changing what it says. It turns patterns collected on Wikipedia into instructions for an agent. Its process includes a first rewrite, a critique and another pass over what still needs work, checked against the original claims.

Packaging that process as a skill makes the criteria inspectable and reusable. A request to “make this more human” leaves plenty of room for the agent to add slang, opinions or an invented anecdote. Written instructions give the editing task clearer boundaries. Their value still depends on how the model follows them and how you review the result.

The Wikipedia guide behind the skill

The source is Wikipedia: Signs of AI writing↗, an advice page from WikiProject AI Cleanup. It collects observed patterns such as inflated significance, superficial analysis and vague attribution. It is neither official Wikipedia policy nor a study certifying authorship.

Its central warning matters: visible signs can point to deeper problems. Removing boldface or repairing formatting without investigating those problems can make detection harder. The page also cautions against relying solely on automated detectors or a reader’s intuition.

Humanizer adapts that material into a rewriting task. For our blog, the practical consequence is to inspect the claim behind each sentence too. If a draft says “experts recommend this tool,” we need to know which experts, where they said it and why. Replacing it with “this tool is recommended” keeps the information gap while removing the attribution that might have alerted us.

How this connects to AI slop

Merriam-Webster defines slop↗, in its digital sense, as low-quality content made with AI, usually in quantity. The term extends beyond writing to images and videos. For a blog, it is a useful way to discuss posts that take up space without giving readers enough information.

Consider a fictional guide promising to help you choose a tool. It repeats that price, ease of use and your needs matter, but never compares options or works through a specific case. You could improve every sentence and still have no idea what to choose. The editorial problem is the missing information.

Applying Humanizer is therefore insufficient to prevent AI slop. The skill can help revise the writing; a useful article also needs a worthwhile question, checked sources and something readers can apply. At LetBrand, that might be a worked example, a comparison with explicit limits or an explanation connecting scattered facts.

Here is the check we suggest: after removing the grand claims, is there an answer left? If not, research the subject or rethink the angle before polishing the prose. The report-export example below does contain specific information, which gives us something worth editing.

What the Humanizer skill does

The blader project on GitHub↗ stores editing instructions in a SKILL.md file. The version reviewed here, 2.11.2, groups 35 patterns and asks the agent to check both the writing and its fidelity to the original. It draws on Wikipedia’s community guide to signs of AI writing.

An agent reads those instructions and applies them using its configured model. The Agent Skills standard↗ describes the format as folders of instructions and resources loaded when needed. Installing Humanizer therefore does not add a new language model to your machine.

The practical value is a repeatable editing task. If you revise documentation, emails or posts every week, you can save your criteria and use them again. Our guide to agent skills explains how these files fit into working with AI.

A before and after with a condition worth protecting

We created this example at LetBrand for the guide. The product is fictional. This is an editorial demonstration, not a performance test or a promise about every model’s output.

Draft:

Our innovative platform marks a pivotal step in transforming report management, delivering a seamless and efficient experience. Administrators can export monthly reports in CSV format. This feature is available exclusively on the Team plan, underscoring our commitment to productivity.

Editorial revision using Humanizer’s criteria:

Administrators on the Team plan can export monthly reports as CSV files. The feature is available only on that plan.

The second sentence deliberately keeps the restriction explicit. Without it, “administrators on the Team plan can” leaves open the possibility that other plans can too. Shortening a text requires attention to these small differences.

The revision removes judgments the draft does not substantiate: that the platform is innovative, that it transforms reporting, or that an export feature proves a commitment to productivity. It keeps who can use the feature, which reports it exports, the format and the restriction.

“Export your reports whenever you want” would sound simple, but it would lose the administrator role, the monthly scope, CSV and the plan restriction. It would also introduce a claim about availability that the original does not establish. That version would be worse despite being shorter.

How to install Humanizer

The repository’s installation instructions↗ use Skills CLI. To add the skill only to your current project, open a terminal in the project folder and run:

npx skills add blader/humanizer

For a user-level installation:

npx skills add blader/humanizer --global

Select the agent you intend to use and reload its skills. Before editing a whole document, check that the agent recognizes Humanizer with a single paragraph. The repository also provides a plugin installation option for Claude Code; follow that route’s instructions if you prefer managing the skill as a plugin.

The Claude Code skills documentation↗ explains slash-command invocation and automatic loading when a skill’s description matches the task. The visible command name can differ between a standalone skill and a plugin.

With the skill installation, you can start with /humanizer and paste your text. You can also ask in plain language to apply Humanizer to the draft. Check that the installed skill is being used; mentioning its name in a conversation does not, by itself, establish that it was loaded.

Give the editor a specific job

Tell the agent what the text is for and who will read it. “Humanize this” leaves it to decide whether it is editing a technical guide, an opinion piece or a product page. A concrete assignment gives you a useful standard for reviewing the result.

Here is a working prompt proposed by LetBrand:

Apply Humanizer to this draft guide for professionals who use AI. Write in natural English with clear examples. Preserve names, numbers, dates, links, quotations and conditions. Do not add personal experiences or product benefits. Flag claims that the supplied material does not support. Return the revised version and identify changes that could affect its meaning.

Add the draft and its sources. If you have writing that represents your voice, include a sample: the project supports using it as a style reference. Aim for a recognizable way of explaining things, without forcing every paragraph into the same rhythm.

In technical writing, repeating a term may be the clearest choice. If we call a product tier the Team plan and later switch to “the collaborative option,” readers must decide whether we still mean the same thing. Variation is useful when it helps them follow the text.

Review the result before publishing

For a blog, we suggest keeping the original beside the edited version and making two separate passes. First, follow the argument as a reader. Then compare the claims individually.

• Find every number, date and name in both versions. Check units, currencies and time periods too.
• Review words that limit a claim: “only,” “up to,” “may,” “in this test” or “according to the manufacturer.”
• Open the sources and check that they support the linked sentence. A preserved link can end up next to a different claim.
• Compare quotations word for word and check that attribution has survived.
• Remove added experiences or results that nobody supplied. When a detail is missing, keep the question open.

In our example, the decisive check is “exclusively on the Team plan.” In a model comparison it might be the benchmark version or reasoning setting. A sentence can become smoother while losing the detail that made the comparison valid.

Where it helps and where more work is needed

Humanizer is worth considering as a second pass when the draft already contains useful information. A more elegant version of an empty article will still leave the reader without an answer. Sources, examples and a clearly defined question need to come before the style pass.

We also do not propose judging authorship by how a text sounds. This guide examines clarity and preservation of meaning; it has not measured detection rates or authorship attribution. Natural writing can contain errors, and formal writing can be accurate.

Start with one section of an article you know well. Save the original, apply the skill and inspect the changes. If you have to repair conditions or recover details, adjust the assignment before extending it to the rest of the blog. That small review will tell you more about its usefulness in your work than a promise of “indistinguishably human” text.

If you are building an editorial workflow with AI, LetBrand can help connect research, writing and review to your website. Tell us what you publish and where the process gets stuck.