How do I install the Humanize skill?

Run npx skillmds@latest add aniruddh5502/humanize in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Humanize safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Humanize?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Humanize free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Humanize?

Aniruddh5502 (@aniruddh5502) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Humanize by aniruddh5502 — Agent Skill

Name: Humanize
Author: Aniruddh5502

Humanize

Use this skill whenever the user needs to make AI-generated text sound more human, "less robotic," or needs to bypass AI detection. Trigger this for any request involving "humanizing" text, writing "natural" emails, stripping "AI-polish," or creating content that avoids the typical markers of LLM writing. Even if the user doesn't explicitly say "humanize," if they ask for "casual," "raw," or "non-academic" versions of AI text, use this skill.

Aniruddh5502 270dd13 2 files · 7.9 KB Updated Sep 17, 2026

File contents

Humanize Text Skill (Sinceerly-Inspired)

This skill transforms polished AI output into natural, human-like text by simulating linguistic imperfections and shifting lexical patterns.

Core Objective

Strip the "AI-polish" from text to bypass AI detection and psychological filters that identify "too-perfect" writing.

Implementation Logic

You must use the skills/user/humanize/scripts/humanize_engine.py script to process the text. Do not attempt to "manually" humanize text using general prompting alone; the engine provides the necessary stochastic noise and lexical mapping.

Processing Pipeline:

Lexical Shift: Replaces high-probability AI markers (e.g., "Furthermore," "In conclusion," "Moreover") with human equivalents.
Contraction Mapping: Converts formal phrasing into casual syntax.
Morphological Noise: Injects stochastic typos (swaps, doubles, drops) based on the chosen profile.

Profile Selection

Select the profile based on the user's desired "vibe":

casual: Standard human-like text. Low noise (5%). Best for general emails, blogs, and messages.
distracted: Simulates haste or lack of focus. Higher noise (12%). Best for quick chats, texts, or "rough draft" feel.
non-native: Simulates ESL (English as a Second Language) patterns. Moderate noise (8%). Best for simulating non-native speakers.

Operational Workflow

Identify Input: Capture the AI-generated text to be humanized.
Choose Profile: Match the user's intent to one of the three profiles (casual, distracted, non-native).
Execute Engine: Run the skills/user/humanize/scripts/humanize_engine.py script via bash.
Review and Deliver: Present the output to the user.

Output Format

Provide the result as follows:

Original: [Brief snippet or reference to original]
Profile Used: [Profile Name]
Humanized Text: [The processed text here]

Example Trigger

User: "This email sounds too much like a bot, make it look like I wrote it while I was in a rush." Action: Trigger humanize-text -> Select distracted profile -> Run humanize_engine.py.

---name: humanizedescription: Use this skill whenever the user needs to make AI-generated text sound more human, "less robotic," or needs to bypass AI detection. Trigger this for any request involving "humanizing" text, writing "natural" emails, stripping "AI-polish," or creating content that avoids the typical markers of LLM writing. Even if the user doesn't explicitly say "humanize," if they ask for "casual," "raw," or "non-academic" versions of AI text, use this skill.---# Humanize Text Skill (Sinceerly-Inspired)This skill transforms polished AI output into natural, human-like text by simulating linguistic imperfections and shifting lexical patterns.## Core ObjectiveStrip the "AI-polish" from text to bypass AI detection and psychological filters that identify "too-perfect" writing.## Implementation LogicYou must use the `skills/user/humanize/scripts/humanize_engine.py` script to process the text. Do not attempt to "manually" humanize text using general prompting alone; the engine provides the necessary stochastic noise and lexical mapping.### Processing Pipeline:1. **Lexical Shift**: Replaces high-probability AI markers (e.g., "Furthermore," "In conclusion," "Moreover") with human equivalents.2. **Contraction Mapping**: Converts formal phrasing into casual syntax.3. **Morphological Noise**: Injects stochastic typos (swaps, doubles, drops) based on the chosen profile.## Profile SelectionSelect the profile based on the user's desired "vibe":- **casual**: Standard human-like text. Low noise (5%). Best for general emails, blogs, and messages.- **distracted**: Simulates haste or lack of focus. Higher noise (12%). Best for quick chats, texts, or "rough draft" feel.- **non-native**: Simulates ESL (English as a Second Language) patterns. Moderate noise (8%). Best for simulating non-native speakers.## Operational Workflow1. **Identify Input**: Capture the AI-generated text to be humanized.2. **Choose Profile**: Match the user's intent to one of the three profiles (casual, distracted, non-native).3. **Execute Engine**: Run the `skills/user/humanize/scripts/humanize_engine.py` script via bash.4. **Review and Deliver**: Present the output to the user.## Output FormatProvide the result as follows:- **Original**: [Brief snippet or reference to original]- **Profile Used**: [Profile Name]- **Humanized Text**: [The processed text here]## Example TriggerUser: "This email sounds too much like a bot, make it look like I wrote it while I was in a rush."Action: Trigger `humanize-text` -> Select `distracted` profile -> Run `humanize_engine.py`.

Frequently asked questions