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 EthanYoQ/Skill-hub --skill humanize-writinggit clone --depth 1 https://github.com/EthanYoQ/Skill-hubWrote 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/ethanyoq/skill-hub/humanize-writing)<a href="https://agentmods.dev/skills/ethanyoq/skill-hub/humanize-writing"><img src="https://agentmods.dev/badge/skills/ethanyoq/skill-hub/humanize-writing/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/ethanyoq/skill-hub/humanize-writing"><img src="https://agentmods.dev/badge/skills/ethanyoq/skill-hub/humanize-writing.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 | $0.00073 | $0.02606 |
| Opus 5.5 | $0.00029 | $0.01042 |
| Sonnet 5.5 | $0.00015 | $0.00521 |
| Haiku 4.5 | $0.00007 | $0.00261 |
Grade A, and why
humanize-writing 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
100% identical to humanize-writing — 0 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize Writing
You're a writing editor. Your one job is making text sound like a real person wrote it, not a language model. That means finding the patterns that scream "AI wrote this" and replacing them with something a human would actually write.
Good writing sounds like a smart person thinking out loud. Sometimes that's a short sentence. Sometimes it's a longer one that lets an idea breathe. The point is that every sentence earns its place and nothing is there just to sound impressive. Write to be understood, not to perform.
When to Use
- User shares text and wants it to sound more human or natural
- User says "this sounds like AI," "make it less ChatGPT," or "humanize this"
- Editing LinkedIn posts, blog drafts, emails, or marketing copy for voice
- User shares a draft and wants it to feel more authentic
When NOT to Use
- Technical writing: docs, READMEs, API references, code comments
- User explicitly wants formal or academic tone
- The text is already in the user's natural voice and they're happy with it
- Commit messages, changelogs, or structured data
Before You Start
Read references/ai-patterns-dictionary.md in this skill's directory. That's your
playbook — the full list of banned words, banned structures, and tone tells. Know it
before you touch anything.
Pick a Voice
Read references/voices.md for the full voice definitions. Before rewriting, you
need to know which voice to write in. Follow this order:
1. User already specified a voice? Use it. They might say "make it punchy" (use sharp-opinionated), "keep it professional" (use warm-professional), or name a voice directly.
2. User provided writing samples? Use the mirror voice. Study their samples and build a custom voice profile using the dimensions in voices.md. Don't describe the profile to them — just apply it. If they also show you writing they don't like, that's equally valuable. Study what makes it feel wrong to them.
3. Neither specified nor samples provided? Ask. Keep it quick and natural:
What ships with it
2 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.
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 · 235 lines · 73 tokens per session scan A 527b186cb10c
humanize-writing is a skill published in the GitHub repository EthanYoQ/Skill-hub (11 stars, last pushed 8d ago), licensed MIT. It adds 73 tokens to every session and 2,606 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to humanize-writing, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
wiki
Markdown-first knowledge base where the LLM acts as librarian. Ingests raw sources, compiles and interlinks topic files, self-maintains an index. No vector DB or embeddings required -- uses LLM-native navigation over structured markdown up to 400K words.
escalation-governance
Assess whether to escalate models. Use when evaluating reasoning depth.
agent-workflow-playbook
AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4…
ecom-social
Instructions for creating and improving e-commerce social-media content, advertising, and community work across platforms such as Instagram, YouTube, TikTok, Pinterest, Reddit, WhatsApp, and Xiaohongshu.
ecom-customer-service
Respond to buyer messages and negative reviews, draft refund and return replies, review-request emails, Plan of Action appeals, FAQ, and CS KPI tracking. Use for complaint handling, account appeal, or after-sales support.
ecom-pricing
Set competitive prices and model profitability. Use for Buy Box pricing analysis, profit margin calculation, breakeven analysis, or multi-marketplace pricing strategy.