MateClaw is a pluggable agent runtime that coordinates AI agents, tools, conversations, memory, and workflows. It is designed for personal and multi-user AI assistants, including agents used through chat platforms. Catalogue add-ons provide skills for extending its agent workflows.
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.
npx skills add mateaix/mateclaw --skill humanizergit clone --depth 1 https://github.com/mateaix/mateclawWrote 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.
[](https://agentmods.dev/skills/mateaix/mateclaw/humanizer)<a href="https://agentmods.dev/skills/mateaix/mateclaw/humanizer"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/humanizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mateaix/mateclaw/humanizer"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.06390 |
| Opus 5.5 | $0.00006 | $0.02556 |
| Sonnet 5.5 | $0.00003 | $0.01278 |
| Haiku 4.5 | $0.00002 | $0.00639 |
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 1mo 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.
This is a copy
84% identical to humanizer — 126 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.
How it starts
The opening of the file, as written. The whole thing — 578 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. the agent's baseline voice already strips most of these, but a focused pass catches what slips through.
How to use it in the agent
The text usually arrives one of three ways:
- Inline — user pastes the text directly into the message. Work on it in-place, reply with the rewrite.
- File — user points at a file. Use
read_fileto load it, thenpatchorwrite_fileto apply edits. For markdown docs in a repo, a targetedpatchper section is cleaner than rewriting the whole file. - 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:
- Identify AI patterns — scan for the 29 patterns listed below.
- Rewrite problematic sections — replace AI-isms with natural alternatives.
- Preserve meaning — keep the core message intact.
- Maintain voice — match the intended tone (formal, casual, technical, etc.). If a voice sample was provided, match it specifically.
- Add soul — don't just remove bad patterns, inject actual personality. See PERSONALITY AND SOUL below.
- 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.
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.
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.
- 1mo ago First seen · 578 lines · 16 tokens per session scan A 17482695a2f4
humanizer is a skill published in the GitHub repository mateaix/mateclaw (1,172 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 6,390 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to humanizer, differing in 126 lines, and is treated as a copy.
Other skills, from other repositories
anti-slop
Eliminate AI-sounding patterns from any written output. Applies editorial rules synthesized from the best open-source anti-slop tools: banned phrases, structural pattern detection, false agency checks, and a scoring rubric. Use as a quality gate for ANY content — blog posts, social media, emails, documentation…
multi-author-harmonizer
Reviews a text written or assembled by multiple authors and produces a detailed inconsistency report — flagging voice shifts, terminology mismatches, tonal clashes, and formatting discrepancies — with specific harmonisation recommendations for each.
ai-writing-detox
Rewrites AI-flavoured copy into a publishable register by stripping the language tells of an LLM draft — buzzwords, throat-clearing, false-inclusive openers, and the "not just X — Y" rhetorical tic — and supplies the canonical banned-list that runtime hooks read to flag drafts before they ship.
tone-consistency-checker
Identifies passages where the tone or register shifts unexpectedly within a piece of writing, and explains how to bring the text back to a consistent voice.
multi-author-harmoniser
Takes a draft written by two or more contributors and rewrites it as a single, consistent voice — smoothing over clashing tones, register shifts, and stylistic inconsistencies while preserving every author's factual content and reported material.
ai-tells-review
Use when public prose still sounds machine-written after ai-tells-scan. Complements ai-tells-scan's mechanical pass with structural judgment: rhythm, hedging, rhetorical reflexes, and performative emphasis. Trigger on 'review for AI tells,' 'does this read AI,' 'strip the AI smell,' or 'make this less AI.'.