A specialized tool for fetching and parsing real-time 13F portfolio holdings and trading activity of elite superinvestors from ValueSider.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install valuesider-superinvestor-data
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install valuesider-superinvestor-data using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ValueSider Superinvestor Data?
ValueSider Superinvestor Data is a robust extension for AI agents designed to bridge the gap between SEC 13F filings and actionable investment insights. By leveraging Openclaw Skills, this tool allows developers and researchers to programmatically retrieve current portfolio compositions and historical trading activity for high-profile fund managers and gurus like Warren Buffett, Seth Klarman, or Bill Ackman.
The skill is specifically engineered to handle the complexities of web data extraction, including bypassing potential access restrictions by utilizing a robust web-fetch and local parsing workflow. This ensures that your AI coding agent can always access the latest data from ValueSider without manual intervention, making it an essential component for any financial analysis toolkit powered by Openclaw Skills.
ValueSider Superinvestor Data Use Cases
- Monitoring specific guru portfolios to identify new 13F holdings and sector allocations.
- Analyzing quarterly buy and sell activity trends for institutional investors.
- Building automated finance dashboards that update based on ValueSider data through Openclaw Skills.
- Identifying high-conviction stock picks by comparing holdings across multiple elite fund managers.
How ValueSider Superinvestor Data Works
- Resolve the specific superinvestor slug by mapping names (e.g., Mason Hawkins) to ValueSider URL paths.
- Fetch the portfolio and activity pages using a web-fetch utility to circumvent standard HTTP request blocks.
- Save the raw HTML content to temporary files for processing or pipe it directly to the parsing engine.
- Execute the provided Python parsing scripts to extract structured JSON data from the HTML.
- Present the synthesized findings, including portfolio value, top holdings, and recent share changes, through the Openclaw Skills interface.
ValueSider Superinvestor Data Setup
To get started with this skill, first install the necessary Python dependencies:
pip install requests beautifulsoup4
If the target site blocks direct requests, use the real-time fetch and parse flow. Save your fetched content and run:
# From the skill directory
python scripts/parse_fetched_content.py --type portfolio --file _portfolio.txt --guru-slug <guru_slug>
For environments where direct fetching is enabled, you can list gurus directly:
python scripts/fetch_valuesider.py --list-gurus --limit 100
ValueSider Superinvestor Data Data Schema & Taxonomy
The skill organizes extracted data into a clean JSON structure, allowing Openclaw Skills to process financial metrics efficiently:
| Object | Fields | Description |
|---|---|---|
| Summary | period, portfolio_value, holdings_count | General overview of the fund's status. |
| Holdings | ticker, stock_name, pct_of_portfolio | Current positions held by the investor. |
| Activity | activity_type, share_change, reported_price | Historical trade data including buys, sells, and adds. |
ValueSider Superinvestor Data Advanced Features
- Intelligent slug resolution to find matching investor profiles automatically.
- Robust error handling for 403 Forbidden responses using secondary web-fetch workflows.
- Support for piped input, allowing for seamless CLI integration without intermediate file storage.
- Detailed metadata extraction including percentage changes to total portfolio value, a core feature for Openclaw Skills users.
SKILL.md
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