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 |

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.

1. Processor (processor.py)
• Reads pending_tweets.json, calls LLM to generate commentary, outputs to drafts.json.
• Requires LLM_API_KEY environment variable.

1. 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