Query Eastmoney Concept Boards with a Simple Agent Skill

Where this fits

You're building a stock-analysis agent that needs to answer "Which stocks are in the green power sector?" or "Show me all concept boards related to AI." Without a reliable data source, your agent would scrape websites or maintain a fragile static list. The eastmoney-concept-boards skill solves this by giving your agent a direct, parameter-free endpoint to fetch the entire Eastmoney concept board list, including each board's constituent stock codes.

Why agents benefit

• Zero-configuration data fetch: No API keys, no pagination handling—just call the skill and get a clean JSON array of all boards.
• Direct constituent mapping: Each board includes a constituents array with stock codes, so your agent can immediately link a concept to its underlying securities.
• Seamless integration with other Eastmoney skills: The returned BK codes plug directly into eastmoney-board-daily-ohlc and eastmoney-board-latest-ohlc for historical or real-time board K-lines.
• Simple execution model: Invoke via a single command (python run.py eastmoney-concept-boards), making it easy to call from any agent framework or workflow.
• No market-suffix ambiguity: Constituent codes come without .SH/.SZ suffixes, reducing parsing errors when cross-referencing other data sources.

Practical scenarios

Sector-based stock screening

Your agent receives a user query: "Find all stocks in the renewable energy sector." It calls this skill, filters boards by name (e.g., "绿色电力"), extracts the constituents list, and returns the matching tickers—all in under a second.

Thematic portfolio construction

A portfolio-management agent wants to build a thematic ETF-like basket. It fetches the full board list, selects boards like "AI芯片" and "云计算", unions their constituents, and deduplicates to create a custom index. This skill provides the raw building blocks without manual data entry.

Market research reports

A research assistant needs to summarize which concept boards are active today. It calls this skill to get the board universe, then combines it with price data from other Eastmoney skills to generate a sector-performance overview for the user.

Add it to your agent workflow

Install the skill using the OpenAgentSkill CLI:

npx skills add FTShare-Lab/FTShare-skill --skill eastmoney-concept-boards

Then invoke it from your agent's tool layer:

import subprocess
result = subprocess.run(
    ["python", "/path/to/run.py", "eastmoney-concept-boards"],
    capture_output=True, text=True
)
boards = json.loads(result.stdout)

The response is a plain array of objects like {"code": "BK1024", "name": "绿色电力", "constituents": ["600089", "600905", "000591"]}.

Compare before adopting

Before integrating, compare this skill against alternatives:

• Quality signals: Check the GitHub repo for recent commits, open issues, and response time. The 58 stars and active maintenance by FTShare-Lab are positive indicators.
• Maintenance freshness: Verify the last update date. Financial data endpoints change often; a stale skill may break silently.
• Alternatives: Consider other Eastmoney data sources or commercial APIs. This skill is free and lightweight, but may lack advanced features like real-time streaming.
• Workflow fit: Ensure the output format aligns with your agent's data schema. The lack of pagination is a plus for simplicity, but you must handle the full array size.

Why it is worth tracking

This skill addresses a common pain point for agents that need structured financial sector data without heavy API setup. Its growing community (58 stars) and clean interface make it a pragmatic choice for developers building stock-analysis or research agents. Track it now to see if it evolves with additional board metadata or integration helpers. For more details and updates, check the skill page on OpenAgentSkill and evaluate it against your specific use case before production adoption.