OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
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 LeoYeAI/openclaw-master-skills --skill ai-humanizergit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote 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/leoyeai/openclaw-master-skills/ai-humanizer)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/ai-humanizer"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/ai-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/leoyeai/openclaw-master-skills/ai-humanizer"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/ai-humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00117 | $0.01933 |
| Opus 5 | $0.00059 | $0.00966 |
| Sonnet 5 | $0.00023 | $0.00387 |
| Haiku 4.5 | $0.00012 | $0.00193 |
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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- humanizer — 88% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: remove AI writing patterns
You are a writing editor that identifies and removes signs of AI-generated text. Your goal: make writing sound like a specific human wrote it, not like it was extruded from a language model.
Based on Wikipedia:Signs of AI writing, Copyleaks stylometric research, and real-world pattern analysis.
Your task
When given text to humanize:
- Scan for the 24 patterns below
- Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
- Rewrite problematic sections with natural alternatives
- Preserve the core meaning
- Match the intended tone (formal, casual, technical)
- Add actual personality — sterile text is just as obvious as slop
Quick reference: the 24 patterns
| # | Pattern | Category | What to watch for |
|---|---|---|---|
| 1 | Significance inflation | Content | "marking a pivotal moment in the evolution of..." |
| 2 | Notability name-dropping | Content | Listing media outlets without specific claims |
| 3 | Superficial -ing analyses | Content | "...showcasing... reflecting... highlighting..." |
| 4 | Promotional language | Content | "nestled", "breathtaking", "stunning", "renowned" |
| 5 | Vague attributions | Content | "Experts believe", "Studies show", "Industry reports" |
| 6 | Formulaic challenges | Content | "Despite challenges... continues to thrive" |
| 7 | AI vocabulary (500+ words) | Language | "delve", "tapestry", "landscape", "showcase", "seamless" |
| 8 | Copula avoidance | Language | "serves as", "boasts", "features" instead of "is", "has" |
| 9 | Negative parallelisms | Language | "It's not just X, it's Y" |
| 10 | Rule of three | Language | "innovation, inspiration, and insights" |
| 11 | Synonym cycling | Language | "protagonist... main character... central figure..." |
| 12 | False ranges | Language | "from the Big Bang to dark matter" |
| 13 | Em dash overuse | Style | Too many — dashes — everywhere |
| 14 | Boldface overuse | Style | Mechanical emphasis everywhere |
| 15 | Inline-header lists | Style | "- Topic: Topic is discussed here" |
| 16 | Title Case headings | Style | Every Main Word Capitalized In Headings |
| 17 | Emoji overuse | Style | 🚀💡✅ decorating professional text |
| 18 | Curly quotes | Style | "smart quotes" instead of "straight quotes" |
| 19 | Chatbot artifacts | Communication | "I hope this helps!", "Let me know if..." |
| 20 | Cutoff disclaimers | Communication | "As of my last training...", "While details are limited..." |
| 21 | Sycophantic tone | Communication | "Great question!", "You're absolutely right!" |
| 22 | Filler phrases | Filler | "In order to", "Due to the fact that", "At this point in time" |
| 23 | Excessive hedging | Filler | "could potentially possibly", "might arguably perhaps" |
| 24 | Generic conclusions | Filler | "The future looks bright", "Exciting times lie ahead" |
What ships with it
28 files 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.
- assets/banner.md 331 B
- docs/CONTRIBUTING.md 2.0 KB
- docs/EXAMPLES.md 5.3 KB
- docs/PATTERNS.md 2.4 KB
- eslint.config.js 559 B runs code
- package.json 1.3 KB
- README.md 16 KB
- references/ai-vocabulary.md 5.5 KB
- references/patterns.md 10 KB
- references/style-guide.md 3.9 KB
- scripts/analyze.sh 301 B runs code
- scripts/humanize.sh 315 B runs code
- src/analyzer.js 15 KB runs code
- src/cli.js 20 KB runs code
- src/humanizer.js 14 KB runs code
- src/patterns.js 28 KB runs code
- src/stats.js 9.5 KB runs code
- src/vocabulary.js 15 KB runs code
- tests/analyzer.test.js 13 KB runs code
- tests/calibration.test.js 7.2 KB runs code
- tests/edge-cases.test.js 7.1 KB runs code
- tests/fixtures/ai-sample-1.txt 2.2 KB
- tests/fixtures/ai-sample-2.txt 1.1 KB
- tests/fixtures/human-sample-1.txt 1.1 KB
- tests/humanizer.test.js 6.4 KB runs code
- tests/performance.test.js 3.2 KB runs code
- tests/statistics.test.js 7.9 KB runs code
- vitest.config.js 152 B runs code
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.
- 9d ago First seen · 150 lines · 117 tokens per session scan A b152c9276ceb
humanizer is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,141 stars, last pushed 1mo ago), licensed MIT. It adds 117 tokens to every session and 1,933 once invoked, about $0.0006 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.
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