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 ztemerbekov/a1-marketing-skills --skill a1-humanizegit clone --depth 1 https://github.com/ztemerbekov/a1-marketing-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/ztemerbekov/a1-marketing-skills/a1-humanize)<a href="https://agentmods.dev/skills/ztemerbekov/a1-marketing-skills/a1-humanize"><img src="https://agentmods.dev/badge/skills/ztemerbekov/a1-marketing-skills/a1-humanize/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/ztemerbekov/a1-marketing-skills/a1-humanize"><img src="https://agentmods.dev/badge/skills/ztemerbekov/a1-marketing-skills/a1-humanize.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 | $0.00065 | $0.06802 |
| Opus 5 | $0.00032 | $0.03401 |
| Sonnet 5 | $0.00013 | $0.01360 |
| Haiku 4.5 | $0.00006 | $0.00680 |
Grade A, and why
a1-humanize scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells. This is a copy
94% identical to humanizer — 549 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 — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: remove AI writing patterns
Rewrite text that sounds AI-generated so it reads like the writer, not a chatbot. Keep the writer's facts, meaning, and voice.
The patterns below come from Wikipedia's "Signs of AI writing", maintained by WikiProject AI Cleanup.
Language
Preserve the language of the rewritten text. Use the user's instruction language for draft labels, audit notes, summaries, and the support footer unless the user asks otherwise. Do not translate supplied text, quotations, names, commands, URLs, or explicit terms.
Scope and sources
Use only text supplied in the conversation, the user's current instructions, and an explicitly supplied writing sample. If no target text is supplied, ask for it. Do not read or overwrite project files or read Marketing Context. When another task uses this skill as one step, return the rewritten prose through embedded mode and let the caller handle the surrounding task.
What to do
When given text to humanize:
- Find AI patterns. Check the text against the patterns below.
- Keep every claim. You may shorten dull parts, expand useful parts, and merge or split paragraphs. Keep the information even when you change the structure.
- Do not invent facts. Do not add a fact, name, number, date, quote, or citation unless it comes from the source or the user. If a sentence needs a missing detail, ask for it or use a simpler sentence. You may add an opinion or reaction when the writer's voice calls for one, but you may not add a factual claim. Fiction is exempt because invented details are part of the task.
- Match the voice. Use the right tone for the text, such as formal, casual, or technical. Add personality only when the text and the writer call for it.
The invocation mode controls what you return. See How to return the result. Use the same rewrite process in every mode.
Match the writer's voice
What ships with it
12 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.
- agents/openai.yaml 363 B
- assets/icon-large.svg 2.3 KB
- assets/icon-small.svg 2.3 KB
- evals/cases/humanize-completed-input-008.md 857 B
- evals/cases/humanize-false-positive-005.md 1.2 KB
- evals/cases/humanize-output-006.md 836 B
- evals/cases/humanize-patterns-001.md 1.2 KB
- evals/cases/humanize-russian-002.md 1.4 KB
- evals/cases/humanize-source-fidelity-003.md 846 B
- evals/cases/humanize-voice-sample-004.md 1.1 KB
- evals/README.md 1.1 KB
- references/license-and-attribution.md 2.0 KB
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.
- 12d ago First seen · 462 lines · 65 tokens per session scan A 3d243a0b5dd4
a1-humanize is a skill published in the GitHub repository ztemerbekov/a1-marketing-skills (8 stars, last pushed 13d ago), licensed MIT. It adds 65 tokens to every session and 6,802 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to humanizer, differing in 549 lines, and is treated as a copy.
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