A specialized rewriting engine designed to remove the robotic AI smell from drafts and optimize them for high-engagement social media platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install content-deai-engine
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install content-deai-engine using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Content De-AI Engine?
The Content De-AI Engine is a sophisticated workflow within the Openclaw Skills ecosystem designed for creators who need to bridge the gap between accurate but lifeless AI-generated drafts and viral, human-centric social media content. It systematically diagnoses AI patterns—such as mechanical transitions, vague adjectives, and lack of stance—and replaces them with authentic first-person experiences, specific technical details, and platform-native structures.
By leveraging this skill, users can ensure their output avoids common machine-generated cliches. The engine focuses on creating content that is not just informationally correct, but also shareable and relatable. It forces the AI to adopt a clear viewpoint and provide actionable insights, making the final result indistinguishable from high-quality human writing across platforms like Xiaohongshu, X, and Zhihu.
Content De-AI Engine Use Cases
- Humanizing generic AI-generated news summaries into opinionated social media posts.
- Removing mechanical templates from automated content pipelines to increase engagement.
- Adapting long-form technical drafts into high-interaction threads for X or Zhihu.
- Transforming textbook-style explanations into oral, relatable content for Xiaohongshu users.
- Refactoring AI drafts that feel too correct or vague into punchy, detail-oriented narratives.
How Content De-AI Engine Works
- Pre-condition Identification: The engine first defines the time window, material source, and target platform to ground the content in reality.
- AI Diagnosis: Analyzes the input for structural and linguistic AI smell, providing a diagnostic grade (High/Medium/Low) based on anti-AI patterns.
- Four-Step Rewrite: Strips mechanical templates, adds at least two real-world details, establishes a clear stance, and provides a call-to-action.
- Platform Adaptation: Applies specific templates for X (counter-intuitive views), Xiaohongshu (personal details/pain points), or Zhihu (evidence/boundaries).
- Standardized Output: Generates A/B titles, the main body, and a strategic first comment for engagement.
- Triangular Self-Check: Subjects the draft to a triple review by a distribution expert, algorithm expert, and a devil's advocate to ensure quality and robustness.
Content De-AI Engine Setup
To implement this within your Openclaw Skills workflow, ensure you have the required reference documents in your skill path.
# Required reference files in your repository:
# references/anti-ai-patterns.md
# references/platform-templates.md
# references/preflight-checklist.md
# Call the skill with a prompt like:
# "Rewrite this draft for X, remove the AI smell, and give me a clear stance."
Content De-AI Engine Data Schema & Taxonomy
The engine organizes output using a strict metadata taxonomy to ensure traceability and quality.
| Attribute | Description |
|---|---|
| Target Audience | Defined demographic for the specific post |
| Core Viewpoint | A single-sentence summary of the post's stance |
| Source Traceability | Origin of data (e.g., ainews, macro, or user-provided) |
| Verification Status | Indicates if the information is 'Verified' or 'To be confirmed' |
| Platform Logic | Specific reasoning for the chosen layout (X, Zhihu, or Xiaohongshu) |
Content De-AI Engine Advanced Features
- Triple-Role Adversarial Review: Uses a 'Devil's Advocate' logic to find and fix weak points in the content's argument.
- Automatic Engagement Seeding: Generates the first comment to steer the conversation and increase algorithm favorability.
- High-Risk Filtering: Built-in boundaries to prevent generating unverified medical or financial advice.
- Contextual Oralization: Adjusts sentence length and rhythm to simulate human speech patterns rather than written prose.
- Multi-Platform A/B Testing: Provides multiple headline options optimized for different platform algorithms.
SKILL.md
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