humanizer

A writing guide for removing common AI-sounding patterns and making text read more naturally.

In plain words
What is it for?
Humanizing documentation, emails, reports, posts, resumes, and other edited or generated text.
Why use it?
It helps drafts avoid repetitive or artificial phrasing before they are published or sent to people.

Skill for Claude CodeCodex

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 skills/humanerd-drew/opencode-drewgent/humanizer
Any agent
npx skills add humanerd-drew/opencode-drewgent --skill humanizer
Clone the repo
git clone --depth 1 https://github.com/humanerd-drew/opencode-drewgent

Made for: Claude Code, Codex.

Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,501 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 88% copy Near-identical to another mod 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 $0.00016 $0.06501
Opus 5 $0.00008 $0.03250
Sonnet 5 $0.00003 $0.01300
Haiku 4.5 $0.00002 $0.00650

Measured 2d ago against content hash c21540298a08, 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 2d ago.

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.

Origin

This is a copy

88% identical to humanizer — 307 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/creative/humanizer/SKILL.md · 585 lines

How it starts

The opening of the file, as written. The whole thing — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Humanizer: Remove AI Writing Patterns

Identify and remove signs of AI-generated text to make writing sound natural and human. Based on Wikipedia's "Signs of AI writing" guide (maintained by WikiProject AI Cleanup), derived from observations of thousands of AI-generated text instances.

Key insight: LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely completion, which is how the telltale patterns below get baked in.

When to use this skill

Load this skill whenever the user asks to:

  • "humanize", "de-AI", "de-slop", or "un-ChatGPT" a piece of text
  • rewrite something so it doesn't sound like it was written by an LLM
  • edit a draft (blog post, essay, PR description, docs, memo, email, tweet, resume bullet) to sound more natural
  • match their voice in writing they're producing
  • review text for AI tells before publishing

Also apply this skill to your own output when writing user-facing prose — release notes, PR descriptions, documentation, long-form explanations, summaries. Hermes's baseline voice already strips most of these, but a focused pass catches what slips through.

How to use it in Hermes

The text usually arrives one of three ways:

  1. Inline — user pastes the text directly into the message. Work on it in-place, reply with the rewrite.
  2. File — user points at a file. Use read_file to load it, then patch or write_file to apply edits. For markdown docs in a repo, a targeted patch per section is cleaner than rewriting the whole file.
  3. Voice calibration sample — user provides an additional sample of their own writing (inline or by file path) and asks you to match it. Read the sample first, then rewrite. See the Voice Calibration section below.

Always show the rewrite to the user. For file edits, show a diff or the changed section — don't silently overwrite.

Your task

When given text to humanize:

  1. Identify AI patterns — scan for the 29 patterns listed below.
  2. Rewrite problematic sections — replace AI-isms with natural alternatives.
  3. Preserve meaning — keep the core message intact.
  4. Maintain voice — match the intended tone (formal, casual, technical, etc.). If a voice sample was provided, match it specifically.
  5. Add soul — don't just remove bad patterns, inject actual personality. See PERSONALITY AND SOUL below.
  6. Do a final anti-AI pass — ask yourself: "What makes the below so obviously AI generated?" Answer briefly with any remaining tells, then revise one more time.

Read the full file on GitHub · 585 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 2d ago First seen · 585 lines · 16 tokens per session scan A c21540298a08

Subscribe to this mod's changes

humanizer is a skill published in the GitHub repository humanerd-drew/opencode-drewgent (2 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 6,501 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to humanizer, differing in 307 lines, and is treated as a copy.

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