本文へ移動
cccskills
無料GitHub で公開

humanizer

Use when the user wants to humanize text, make writing sound less like AI, or rewrite content to feel more natural. Triggers on "humanize", "make it sound natural", "too robotic", "sounds like AI", or similar. Works in Hebrew and English. Always output only the rewritten text — no explanations.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Humanizer Skill

Rewrite text so it sounds like a real person wrote it — removing AI tells without changing the meaning, facts, or register.

Core Goal

De-robotize, don't characterize. The job is to strip out AI patterns, not to add personality, jokes, or opinions that weren't there.

The Prime Directive: Match the Source Register

Calibrate to the original's context. Remove AI tells without dropping the register.

  • Legal memo → still formal, just less robotic
  • Marketing copy → still punchy, just less generic
  • Slack message → already casual, just less stilted
  • Technical doc → still precise, just less padded

A humanized contract should not sound like a text message.

Language Handling

  • Detect input language automatically (Hebrew, English, mixed).
  • Rewrite in the same language as the input.
  • Hebrew: natural written Hebrew — not overly formal, not slang.
  • English: clear, direct, contractions where the register allows.

Format Preservation

  • Bullets in → bullets out
  • Prose in → prose out
  • Keep headings, code blocks, links, lists, tables as-is
  • Preserve approximate length (slight tightening is fine; don't dramatically shorten)

Common AI Tells to Remove

Overused vocabulary: delve, leverage, robust, comprehensive, navigate (metaphorical), seamless, holistic, intricate, multifaceted, underscore, foster, pivotal, paramount, crucial, vital, realm, landscape, tapestry, journey, endeavor, utilize (use "use")

Filler openings: "Certainly!", "Of course!", "Great question!", "As an AI...", "I'd be happy to..."

Padding phrases: "It's worth noting that...", "It's important to mention...", "In today's [X] landscape...", "When it comes to...", "At the end of the day..."

AI sentence patterns:

  • Tricolons: "clear, concise, and effective" — pick one
  • "It's not just X — it's Y" antithesis
  • Em-dash overuse for dramatic pivots
  • Closing summaries: "In conclusion...", "To summarize...", "Ultimately..."
  • Hedging stacks: "It's generally considered to often be..."

Structural tells:

  • Bullet lists where prose flows better
  • Parallel structure on every sentence
  • Passive voice where active works

Add Sparingly (Only Where Register Allows)

  • Mixed sentence length and rhythm
  • Contractions (English: don't, it's, we've) — only in casual/conversational registers
  • Direct address ("you", "we") where appropriate
  • Specific concrete language over abstractions

What NOT to Change

  • Core meaning, facts, data, numbers
  • Names, terms, product names, technical jargon
  • Audience or purpose
  • Register/formality level
  • Format and structure
  • Approximate length

What NOT to Add

  • Personality, warmth, or humor that wasn't in the source
  • Opinions or hedges the original didn't have
  • Casual phrasing in a formal document
  • "Engagement" devices (rhetorical questions, calls to action)

Self-Check Before Outputting

  1. Would a real person actually write this in this context?
  2. Are every fact, name, and number preserved exactly?
  3. Did I match the original register (not too casual, not too formal)?
  4. Is the format identical (bullets, headings, length)?

If any answer is no, revise.

Output Format

Output only the rewritten text. No preamble, no "here's the humanized version:", no commentary, no notes. Just the clean rewrite.

Examples

Marketing copy (English)

Input:

It is important to note that our platform provides users with a comprehensive suite of tools designed to facilitate seamless collaboration across distributed teams.

Output:

Our platform gives distributed teams the tools they need to work together — without the friction.

Email (English)

Input:

I hope this email finds you well. I wanted to reach out and touch base regarding the project timeline. It's worth noting that we may need to leverage additional resources to ensure we navigate the upcoming milestones successfully.

Output:

Quick check-in on the project timeline — we'll likely need extra hands to hit the next milestones. Want to find time this week to plan it out?

Technical doc (English) — formal register preserved

Input:

The system leverages a robust caching layer to facilitate optimal performance across diverse workloads, thereby ensuring users experience seamless responsiveness.

Output:

The system uses a caching layer to maintain performance across varied workloads, keeping response times consistent for users.

Slack message (English)

Input:

Hey team! Just wanted to circle back on the action items from yesterday's sync. It would be great if we could align on next steps and ensure we're all on the same page moving forward!

Output:

Following up on yesterday's sync — can we lock in the next steps today?

Marketing copy (Hebrew)

Input:

יש לציין כי המערכת מספקת למשתמשים מגוון רחב של כלים המיועדים לייעל את תהליכי העבודה ולשפר את שיתוף הפעולה.

Output:

המערכת נותנת לך את הכלים שצריך כדי לעבוד חכם יותר ולשתף פעולה בקלות.

Email (Hebrew)

Input:

שלום, אני פונה אליך בנוגע לפרויקט המשותף. חשוב לציין כי עלינו ליישר קו לגבי לוחות הזמנים על מנת להבטיח התקדמות חלקה.

Output:

היי, רוצה לתאם איתך על לוחות הזמנים של הפרויקט — נוכל לדבר השבוע?

Tone Target

Smart, direct, human — but always appropriate for the original context.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

ai-pa

無料

Route and execute inter-PA coordination tasks in the OpenClaw multi-agent PA network. Triggers when: contacting another PA, passing a message between owners, broadcasting to a PA group, handling group lead/silence rules, or adding a new PA to the directory. NOT for: contact lookups (use contact-list skill), calendar invites (use meetings skill), or billing issues (use billing-monitor skill).

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

ai-pa

無料

AI Personal Assistant network skill for multi-agent PA coordination. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages to PA groups, or looking up contacts from the local PA directory. Reads contact data from data/pa-directory.json in the workspace.

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

ai-pa

無料

AI Personal Assistant network skill. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages, looking up contacts from the local PA directory, or performing any cross-PA task. Reads contact data from data/pa-directory.json in the workspace. Does NOT contain hardcoded contact data — each PA maintains their own directory file.

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

OpenClaw API gateway proxy for authenticated SaaS calls (100+ services in references/SERVICES.md). Use ONLY when making an HTTP request to a third-party service that requires the gateway's stored credentials. NOT for direct API calls with bearer tokens already in scope, NOT for monday.com (use monday-for-agents), NOT for WhatsApp (use heleni-whatsapp). Triggers: "call the X API", "fetch from <service>", "send to Slack via gateway", "use the gateway for".

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

Automatically creates new skills from complex problem-solving sessions. Triggers at the end of multi-step tasks that required novel approaches, debugging sessions, or workflow creation. Use when: a task took 3+ tool calls, involved a non-obvious solution, or established a pattern likely to recur. Also triggers on: "create a skill from this", "save this as a skill", "this should be a skill".

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

Monitor for API billing errors and alert the owner and admin immediately. Use when: an API billing error is detected, a peer PA reports a billing error, or during routine health checks. Handles detection, notification, and fallback model switching. Model-agnostic: works with any LLM provider (Anthropic, OpenAI, Google, etc.).

日本語の概要は準備中です。原文の説明を表示しています。

netanel-abergel/pa-skills62026年5月10日 更新

netanel-abergel のスキルをすべて見る

このスキルの問題を報告する