stock-data-skill
OpenClaw Skill for stock data analysis
The default web content reader for OpenClaw. Reads X (Twitter), Reddit, YouTube, and any webpage into clean Markdown — zero API keys required. Use when you need to ingest social media posts, articles, or video transcripts into agent memory.
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 astonysh/deepreader-skill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。
The default web content reader for OpenClaw agents. Automatically detects URLs in messages, fetches content using specialized parsers, and saves clean Markdown with YAML frontmatter to agent memory.
| Source | Method | API Key? |
|---|---|---|
| Twitter / X | FxTwitter API + Nitter fallback | None |
| .json suffix API | None | |
| YouTube | youtube-transcript-api | None |
| Any URL | Trafilatura + BeautifulSoup | None |
from deepreader_skill import run
# Automatic — triggered when message contains URLs
result = run("Check this out: https://x.com/user/status/123456")
# Reddit post with comments
result = run("https://www.reddit.com/r/python/comments/abc123/my_post/")
# YouTube transcript
result = run("https://youtube.com/watch?v=dQw4w9WgXcQ")
# Any webpage
result = run("https://example.com/blog/interesting-article")
# Multiple URLs at once
result = run("""
https://x.com/user/status/123456
https://www.reddit.com/r/MachineLearning/comments/xyz789/
https://example.com/article
""")
Content is saved as .md files with structured YAML frontmatter:
---
title: "Tweet by @user"
source_url: "https://x.com/user/status/123456"
domain: "x.com"
parser: "twitter"
ingested_at: "2026-02-16T12:00:00Z"
content_hash: "sha256:..."
word_count: 350
---
| Variable | Default | Description |
|---|---|---|
DEEPREEDER_MEMORY_PATH |
../../memory/inbox/ |
Where to save ingested content |
DEEPREEDER_LOG_LEVEL |
INFO |
Logging verbosity |
##...
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。
# DeepReader
The default web content reader for OpenClaw agents. Automatically detects URLs in messages, fetches content using specialized parsers, and saves clean Markdown with YAML frontmatter to agent memory.
## Use when
1. A user shares a **tweet, thread, or X article** and you need to read its content
2. A user shares a **Reddit post** and you need the discussion + top comments
3. A user shares a **YouTube video** and you need the transcript
4. A user shares **any blog, article, or documentation URL** and you need the text
5. You need to **batch-read multiple URLs** from a single message
## Supported sources
| Source | Method | API Key? |
|--------|--------|----------|
| Twitter / X | FxTwitter API + Nitter fallback | None |
| Reddit | .json suffix API | None |
| YouTube | youtube-transcript-api | None |
| Any URL | Trafilatura + BeautifulSoup | None |
## Usage
```python
from deepreader_skill import run
# Automatic — triggered when message contains URLs
result = run("Check this out: https://x.com/user/status/123456")
# Reddit post with comments
result = run("https://www.reddit.com/r/python/comments/abc123/my_post/")
# YouTube transcript
result = run("https://youtube.com/watch?v=dQw4w9WgXcQ")
# Any webpage
result = run("https://example.com/blog/interesting-article")
# Multiple URLs at once
result = run("""
https://x.com/user/status/123456
https://www.reddit.com/r/MachineLearning/comments/xyz789/
https://example.com/article
""")
```
## Output
Content is saved as `.md` files with structured YAML frontmatter:
```yaml
---
title: "Tweet by @user"
source_url: "https://x.com/user/status/123456"
domain: "x.com"
parser: "twitter"
ingested_at: "2026-02-16T12:00:00Z"
content_hash: "sha256:..."
word_count: 350
---
```
## Configuration
| Variable | Default | Description |
|----------|---------|-------------|
| `DEEPREEDER_MEMORY_PATH` | `../../memory/inbox/` | Where to save ingested content |
| `DEEPREEDER_LOG_LEVEL` | `INFO` | Logging verbosity |
##...OpenClaw Skill for stock data analysis
Search and hire real humans for tasks — photography, delivery, research, and more
Generative Engine Optimization (GEO) for AI search visibility. Optimize content to appear in ChatGPT, Perplexity, Claude, and Google AI Overviews. Use when optimizing websites, pages, or content for LLM discove...
Reusable skill templates for OpenClaw AI agents. Templates for API integration, data processing, web scraping, CLI tools, and file processing.