A comprehensive financial data skill for retrieving A-share performance express reports, forecasts, and core financial statements across the Chinese market.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ftshare-performance-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 ftshare-performance-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 FTShare A-Share Performance Data?
FTShare-ashare-performance-data is a powerful unified routing entry designed for AI agents to access detailed financial metrics of the A-share market. It serves as a central hub for Openclaw Skills to query performance express reports, profit forecasts, and the three primary financial statements: cash flow, income, and balance sheets. By interfacing with market.ft.tech, it provides both single-stock historical views and full-market snapshots for specific reporting periods.
This skill is essential for developers and financial analysts building AI tools that require high-fidelity financial data. It simplifies complex HTTP GET requests into a structured command-line interface, ensuring that Openclaw Skills can accurately parse and present stock performance data to end-users in real-time.
FTShare A-Share Performance Data Use Cases
- Analyzing historical performance express reports for a specific A-share stock code.
- Fetching market-wide performance forecasts for a designated report type like annual or quarterly.
- Comparing cash flow and income statements across multiple companies for a specific year.
- Retrieving the balance sheet of a single stock to evaluate its financial health over time.
- Building automated financial dashboards that require periodic updates of A-share market data.
How FTShare A-Share Performance Data Works
- The AI agent identifies the user intent, such as a request for a specific stock's income statement.
- The system locates the run.py script by replacing the SKILL.md suffix in the absolute path.
- The agent matches the intent to one of the specialized sub-skills, such as stock-income-single-stock-all-periods.
- A subprocess call is made using the Python runtime with required arguments like stock-code or year.
- The skill fetches the JSON data from market.ft.tech and returns it to the agent for formatting and display.
FTShare A-Share Performance Data Setup
To use this within the Openclaw Skills ecosystem, ensure you have the Python environment configured and access to the local file system. Use the run.py entry point for all sub-skill executions:
# Navigate to the skill directory
cd path/to/FTShare-ashare-performance-data
# Example: Query 2025 Q2 market-wide performance express
python run.py stock-performance-express-all-stocks-specific-period --year 2025 --report-type q2 --page 1 --page-size 20
# Example: Query historical cash flow for a single stock
python run.py stock-cashflow-single-stock-all-periods --stock-code 000001.SZ
FTShare A-Share Performance Data Data Schema & Taxonomy
The skill returns structured JSON objects. Key data structures include:
| Data Category | Sub-Skill Mapping | Required Parameters |
|---|---|---|
| Performance Express | stock-performance-express-* | --year, --report-type OR --stock-code |
| Forecasts | stock-performance-forecast-* | --year, --report-type OR --stock-code |
| Financial Statements | stock-cashflow-, stock-income-, stock-balance-* | --stock-code OR --year/--report-type |
Pagination is supported for market-wide queries using --page and --page-size parameters.
FTShare A-Share Performance Data Advanced Features
- Unified routing architecture that dynamically locates sub-skills using path-based resolution.
- Support for multiple report types including annual, q1, q2, and q3.
- Seamless integration with Openclaw Skills for multi-agent financial research workflows.
- High-performance data fetching from the market.ft.tech API with optimized JSON parsing.
SKILL.md
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