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 xiaohei-info/oh-my-multica --skill creative-humanizergit clone --depth 1 https://github.com/xiaohei-info/oh-my-multicaWrote 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/xiaohei-info/oh-my-multica/creative-humanizer)<a href="https://agentmods.dev/skills/xiaohei-info/oh-my-multica/creative-humanizer"><img src="https://agentmods.dev/badge/skills/xiaohei-info/oh-my-multica/creative-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/xiaohei-info/oh-my-multica/creative-humanizer"><img src="https://agentmods.dev/badge/skills/xiaohei-info/oh-my-multica/creative-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 | $0.00022 | $0.07606 |
| Opus 5 | $0.00011 | $0.03803 |
| Sonnet 5 | $0.00004 | $0.01521 |
| Haiku 4.5 | $0.00002 | $0.00761 |
Grade A, and why
creative-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 11d 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
83% identical to humanizer — 215 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 — 653 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
- audit a draft for AI tells without rewriting it yet
Also apply this skill to your own output when writing user-facing prose such as release notes, pull-request descriptions, documentation, long-form explanations, and summaries. A focused pass catches patterns that general editing often misses.
How to use it
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.
Before editing, determine two things:
- Mode —
rewrite(default) ordetect/ audit-only - Context profile — general, linkedin, technical-blog, investor-email, docs, or casual
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.
- 11d ago First seen · 653 lines · 22 tokens per session scan A bdfc01d531ec
creative-humanizer is a skill published in the GitHub repository xiaohei-info/oh-my-multica (26 stars, last pushed 3d ago), licensed MIT. It adds 22 tokens to every session and 7,606 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to humanizer, differing in 215 lines, and is treated as a copy.
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