trend-to-redbook-automation

name	trend-to-redbook-automation
description	End-to-end automation orchestrator. Fetches data from Twitter (X) or global News MCPs, passes it through the detoxifier for compliance, formats it via the content-creator, and renders/publishes to Redbook using the local render engine.

🌟 Trend to Redbook Automation Workflow

Core Definition

This acts as a "Workflow Orchestrator". It does not perform physical work itself, but schedules and coordinates operations across four distinct modules, creating a complete, no-code AI automation pipeline.

Prerequisites

To run this pipeline perfectly, you need to install the following underlying skill modules in OpenClaw/your system:

1. Data Acquisition Layer (Install at least one):
   • opentwitter-mcp (from infra403, for stable Twitter fetching)
   • opennews-mcp (from infra403, for stable news sourcing)
2. Processing & Publishing Layer (Required standard suite):
   • redbook-anti-risk-detoxifier-skills (Compliance formatting & deduplication)
   • redbook-content-creator-skills (Redbook viral copywriting structure)
   • redbook-render-skills (Local image rendering & publishing engine)

---

Automation Pipeline SOP (Execution Standard for Agents)

When the user issues a command (e.g., "Help me migrate @cryptoxiao's latest tweet to Redbook"), the Agent must strictly and sequentially execute the following four stages in the background. Do not arbitrarily interrupt or ask questions unless an error occurs:

🎯 Stage 1: Information Acquisition

• Call the appropriate MCP interface based on the user's source command.
• If the target is a Twitter influencer (e.g., @cryptoxiao), silently call opentwitter-mcp to get their latest tweet (Text/Media).
• If the target is global news, call opennews-mcp to fetch the news content.
• Output: Obtain high-density Raw_Text.

🎯 Stage 2: Compliance Detoxification

• Pass the Raw_Text to redbook-anti-risk-detoxifier-skills.
• Strictly follow its ban-word list and detoxification strategy (e.g., rewriting high-risk terms like "VPN", "Twitter scraping", "Crypto" into localized neutral phrases like "cloud assistants" and "information arbitrage").
• Output: Obtain a sanitized and compliant Safe_Text.

🎯 Stage 3: Viral Content Creation

• Feed the Safe_Text into redbook-content-creator-skills.
• Format the content following the CES algorithm rules for strong emotional pacing.
• Output: A structured final draft, including a highly suspenseful Title, a platform-native Body, and high-traffic Tags.

🎯 Stage 4: Rendering & Publishing

• Locate the root directory of redbook-render-skills (formerly Auto-Redbook-Skills).
• Inject the title and body generated in the previous step, and call its core render_xhs.py or render_xhs.js to batch-render stacked graphic cards.
• Finally, execute the publishing script or prompt the user to review the beautifully rendered card set in the local output directory.

---

Why Design It This Way?

Novice users or operators only need to trigger this skill with a single sentence. The AI will act as the master contractor of the pipeline, automatically dispatching the Twitter data fetcher, the compliance filter, the copywriter, and the graphic renderer, delivering a finished product ready for Redbook publishing within a minute.