An AI-powered content engine that converts technical documents, PDFs, and research papers into structured, engagement-ready Xiaohongshu posts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install xhs-post-factory
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 xhs-post-factory 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 XHS Post Factory?
XHS Post Factory is a technical content transformation tool designed to bridge the gap between complex source materials and social media platforms. It specifically targets Xiaohongshu (XHS), enabling developers and researchers to turn academic papers, markdown files, and JSON data into high-quality posts. As part of the Openclaw Skills ecosystem, it focuses on technical accuracy and structured output, ensuring that content is both professional and platform-optimized without the risk of AI hallucination.
The tool operates on a strict contract-based input/output model. It parses source files like PDFs and identifies core research components to generate standardized sections, including problem statements, core methodologies, and experimental results. By leveraging customizable templates, users can maintain a consistent voice while automating the tedious aspects of social media formatting, such as emoji placement and hashtag generation.
XHS Post Factory Use Cases
- Summarizing academic research papers for social media followers.
- Converting technical product documentation into accessible digital marketing snippets.
- Batch processing JSON data feeds into structured Xiaohongshu post formats.
- Streamlining the content workflow for Openclaw Skills users who manage multiple technical social media accounts.
How XHS Post Factory Works
- Input Scanning: The skill identifies the source file type and checks for existing parsed products (like paper-card.md) to maximize information density.
- Template Routing: It selects the appropriate template (defaulting to paper-interpretation) based on user intent or explicit configuration.
- Structured Extraction: The engine extracts specific data points such as the research problem, methodology highlights, and experimental conclusions.
- Style Application: It applies Xiaohongshu-specific styling, including emoji density adjustments and title candidate generation.
- Output Finalization: The system generates two distinct files, xhs-post.md and xhs-post.json, containing the formatted post and its associated metadata.
XHS Post Factory Setup
To utilize this within your Openclaw Skills setup, ensure the skill is registered and the template directory is accessible.
# Clone or move the skill to your agent's skill path
cp -r xhs-post-factory /your/agent/skills/
# Ensure the templates directory exists for routing
mkdir -p templates/
Configuration parameters like length and emoji_density can be passed during invocation to customize the output style.
XHS Post Factory Data Schema & Taxonomy
The skill produces structured data to ensure consistency across different platforms and integrations:
| Field | Type | Description |
|---|---|---|
post_type |
String | The template used (e.g., paper-interpretation) |
title_candidates |
Array | 3-5 catchy title options for the user to choose from |
final_title |
String | The primary selected title for the post |
body_sections |
Object | The structured segments of the post (Problem, Method, Results) |
emoji_density_level |
String | Setting for emoji frequency (low/medium/high) |
evidence_notes |
Array | Internal logs mapping generated content back to source file segments |
XHS Post Factory Advanced Features
- Multi-template support allowing for custom post types beyond paper interpretation.
- Intelligent fallback mechanism that flags missing information as "original text not specified" instead of hallucinating data.
- Granular control over emoji density and post length to match specific brand identities.
- Evidence tracking that provides a transparent link between the generated social copy and the source technical document.
- Integration ready for Openclaw Skills workflows requiring automated JSON-to-Social pipelines.
SKILL.md
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