---

name: social-reader description: Social media content scraping and automation skill. Supports real-time single post reading, as well as scheduled batch patrol, LLM distillation, and review notifications.

Social Reader Skill

This skill provides a social media content scraping and monitoring workflow. It offers two usage modes:

• Interactive Mode: Agent fetches a single post in real-time for reading, discussion, or reply generation within a conversation.
• Pipeline Mode: Background batch patrol of sources, with LLM distillation and review notifications.

Dependencies

pip install requests

Configuration Files

File	Purpose
prompt.txt	LLM system prompt for the Processor node
sources.json	List of monitored accounts and fetch intervals (pipeline mode)
input_urls.txt	Manually entered post URLs (one per line, # for comments)
seen_ids.json	Deduplication cache for seen post IDs (pipeline mode only)
pending_tweets.json	Queue of unprocessed posts from the Watcher
drafts.json	LLM-distilled drafts from the Processor
archive.json	Archived history records

Environment Variables (required only for Pipeline Mode Processor)

Variable	Description	Default
LLM_API_KEY	LLM API key (required)	None
LLM_BASE_URL	API endpoint	https://api.openai.com/v1
LLM_MODEL	Model name	gpt-4o-mini

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Mode 1: Agent Interactive Call (Recommended)

When a user sends a social media post link and asks you to "read and discuss" or "generate a quality reply", call fetcher.py directly — do NOT use run_pipeline.py.

run_pipeline.py triggers deduplication cache, fixed LLM distillation, and browser popups, which are unsuitable for interactive scenarios.

Usage Example

import sys

skill_dir = r"d:\AIWareTop\Agent\openclaw-skills\social-reader"
if skill_dir not in sys.path:
    sys.path.append(skill_dir)

from fetcher import get_tweet

result = get_tweet("https://x.com/user/status/123456")

if result.get("success"):
    content = result["content"]
    # Now you can discuss the content with the user or generate a reply

get_tweet() Return Structure

{
  "source": "fxtwitter",
  "success": true,
  "type": "tweet",
  "content": {
    "text": "Post body text",
    "author": "Display name",
    "username": "Username handle",
    "created_at": "Publish time",
    "likes": 123,
    "retweets": 45,
    "views": 6789,
    "replies": 10,
    "media": ["image_url_1", "image_url_2"]
  }
}

When type is "article" (long-form post), content additionally contains:

• title: Article title
• preview: Preview text
• full_text: Full article body (Markdown format)
• cover_image: Cover image URL

This call is completely stateless — it writes no cache files and triggers no notification services.

---

Mode 2: Background Pipeline Batch Processing

Use run_pipeline.py to chain Watcher → Processor → Action nodes. Suitable for scheduled tasks or batch processing.

Three Core Nodes

1. Watcher (watcher.py)

   • Reads input_urls.txt or sources.json, deduplicates via seen_ids.json, writes new posts to pending_tweets.json.

2. Processor (processor.py)

   • Reads pending_tweets.json, calls LLM to generate commentary, outputs to drafts.json.
   • Requires LLM_API_KEY environment variable.

3. Action (notifier.py)

   • Starts a local HTTP review server (port 18923), opens a browser review page with approve/reject/rewrite/archive controls.

CLI Examples

# Full pipeline
python run_pipeline.py

# Specific URL
python run_pipeline.py https://x.com/elonmusk/status/123456

# Single node execution
python run_pipeline.py --watch-only
python run_pipeline.py --process-only
python run_pipeline.py --notify-only