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 GPTHuman-ai/mcp-server --skill gpthuman-mcpgit clone --depth 1 https://github.com/GPTHuman-ai/mcp-serverWrote 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/gpthuman-ai/mcp-server/gpthuman-mcp)<a href="https://agentmods.dev/skills/gpthuman-ai/mcp-server/gpthuman-mcp"><img src="https://agentmods.dev/badge/skills/gpthuman-ai/mcp-server/gpthuman-mcp/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/gpthuman-ai/mcp-server/gpthuman-mcp"><img src="https://agentmods.dev/badge/skills/gpthuman-ai/mcp-server/gpthuman-mcp.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 | $0.00027 | $0.00531 |
| Opus 5.5 | $0.00011 | $0.00212 |
| Sonnet 5.5 | $0.00005 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
GPTHuman-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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPTHuman Text Humanizer
This skill enables you to use the humanize_text tool provided by the GPTHuman MCP server to rewrite AI-generated text into natural, human-sounding prose with AI-detector metadata.
When to Use
Use this skill when the user asks you to:
- "Humanize" a piece of text
- Rewrite AI-generated content to sound more human
- Make text bypass AI detectors or improve its human score
- Adjust the tone of an AI-generated draft to sound more natural
How to Use
If the GPTHuman MCP server is connected, you will have access to the humanize_text tool.
Call the humanize_text tool with the following parameters:
text(required): The text you want to humanize. Note: The text must be at least 300 characters and at most 2,000 words.tone(optional): The target reading level / tone. Valid options areStandard,HighSchool,College,PhD. Defaults toCollege.mode(optional): The rewrite strategy. Valid options areProfessional,Balanced,Enhanced. Defaults toBalanced.
Steps
- Verify Text Length: Ensure the text provided by the user is at least 300 characters long. If it's too short, inform the user that GPTHuman requires a minimum of 300 characters for optimal results.
- Determine Tone and Mode: If the user hasn't specified a tone or mode, use your best judgment based on the context, or use the defaults (
CollegeandBalanced). - Execute the Tool: Call
humanize_textwith the appropriate arguments. - Present the Results: Provide the humanized text to the user. You should also briefly mention the key metadata returned by the tool (such as the AI-detector Human Score, Similarity, and Credit Usage).
Example Scenarios
-
Scenario 1: The user says "Humanize this generated blog intro in College tone using Balanced mode."
- Action: Call
humanize_textwith the user's text,tone="College", andmode="Balanced".
- Action: Call
-
Scenario 2: The user asks "Rewrite this product description in Professional mode."
- Action: Call
humanize_textwith the user's text andmode="Professional".
- Action: Call
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.
- 2mo ago First seen · 41 lines · 27 tokens per session scan A e76ede08d08c
GPTHuman-humanizer is a skill published in the GitHub repository GPTHuman-ai/mcp-server (1 stars, last pushed 8d ago), licensed Apache-2.0. It adds 27 tokens to every session and 531 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
GPTHuman-humanizer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| GPTHuman-humanizerGPTHuman-ai/mcp-server | Skill | 1 | 8d ago | Apache-2.0 | A | 27 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |