humanizer

humanizer is a command for coding agents from shir-danishyar/humanize. It costs 22 tokens per session (853 once invoked), scanned A, original, MIT.

A text-rewriting command that removes common signs of AI-generated writing while keeping the original meaning.

In plain words
What is it for?
Rewriting pasted text or the prose in a file while leaving code, data, links, and other protected content unchanged.
Why use it?
It helps prose sound more natural without changing its claims, citations, numbers, or other important content.

Command

Part of the humanize-writing plugin — 1 skill, 1 command shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/shir-danishyar/humanize/humanizer
Clone the repo
git clone --depth 1 https://github.com/shir-danishyar/humanize

Or install humanize-writing, the plugin that ships this one along with the rest of its 1 skill, 1 command.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for humanizer

README.md
[![agentmods](https://agentmods.dev/badge/commands/shir-danishyar/humanize/humanizer.svg)](https://agentmods.dev/commands/shir-danishyar/humanize/humanizer)
Your own site
<a href="https://agentmods.dev/commands/shir-danishyar/humanize/humanizer"><img src="https://agentmods.dev/badge/commands/shir-danishyar/humanize/humanizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 853 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00853
Opus 5 $0.00011 $0.00426
Sonnet 5 $0.00004 $0.00171
Haiku 4.5 $0.00002 $0.00085

Measured yesterday against content hash ce3082cf8b08, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

humanizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/humanizer.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Humanize the text below (or the file it names) using the humanize-writing skill in rewrite mode.

If the humanize-writing skill is installed, load it now and follow it in full, including references/patterns.md and references/vocabulary.md. If it is not available, apply the rules summarized here.

Input

$ARGUMENTS

If the input is a file path, read the file and rewrite only its prose: leave code blocks, front matter, data, and link targets untouched, then write the result back to the same file. If the input is empty, ask the user to paste the text or give a file path. Do nothing else.

Rules (rewrite mode)

Preserve everything except the patterns:

  • Never alter direct quotes; quoted third-party text stays verbatim.
  • Never change citations, numbers, statistics, dates, URLs, or proper nouns.
  • Keep every claim the text makes. Change phrasing, not meaning.
  • Never add a fact, name, number, date, quote, or citation that is not in the source. If a sentence needs a specific you don't have, leave the sentence plainer rather than invent one.
  • Detect the register (social / email / editorial / formal) and keep it. Contractions and fragments are fine in a post; neither belongs in a formal report.
  • If a voice-profile.md exists in the project, match it.

Strip, in order of damage:

  1. Negative parallelism: "It's not X, it's Y", "not only X but also Y", "X rather than Y". State the positive claim.
  2. Trailing significance clauses: "..., reflecting / highlighting / underscoring / ensuring / contributing to ...". State the fact and stop.
  3. Significance inflation and puffery: pivotal moment, enduring legacy, setting the stage for, testament to, nestled, vibrant, renowned, rich heritage.
  4. AI vocabulary: delve, tapestry, intricate, interplay, crucial, pivotal, key (adj.), leverage, robust, seamless, foster, enhance, align with, showcase, underscore, empower, streamline, holistic, comprehensive, transformative, cutting-edge, game-changer, myriad, plethora, deep dive, ever-evolving.
  5. Copula avoidance: "serves as", "stands as", "boasts", "features", "offers" → "is", "has".
  6. Rule of three: keep the item that carries information; drop the padding.
  7. Vague attribution and canned coverage: "experts say", "studies show", "featured in national media outlets", "maintains an active social media presence". Name the source or cut.
  8. Vague association: "associated with", "in connection with" → the actual relationship (founded, taught, member of).
  9. Summary closers ("In conclusion", "Overall", "Ultimately"), editorializing ("It's important to note"), challenges-and-outlook endings. End on the last substantive point.
  10. Chatbot residue: "Great question", "You're absolutely right", "I hope this helps", "Let me know if", "Would you like me to", "As of my last update", "While specific details are not widely documented" followed by a guess. Delete.
  11. Formatting: at most one em dash per ~300 words and unspaced; no bold mid-sentence; no **Term:** definition bullets; no emoji in headings; sentence-case headings; no headings under ~400 words; consistent quotation marks; no two-row tables.
  12. Rhythm: vary sentence length; say each idea once.

Read the full file on GitHub · 48 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 48 lines · 22 tokens per session scan A ce3082cf8b08

Subscribe to this mod's changes

humanizer is a command published in the GitHub repository shir-danishyar/humanize (15 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 853 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.