OpenClaw

social-reader

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

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install hacksing/social-reader

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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["conte...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/hacksing/social-reader
Version: —
Commit: 796e4be682a86d068de1c384a1a9b453602e9983
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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["conte...

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