social-reader

# 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 ```bash 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 ```python 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 ```json { "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 ```bash # 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 ```