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Estimate the distribution of stockholders' purchase costs for Chinese A-share stocks using price and volume data. A triangular model produces cost concentration metrics, the estimated share below the current price, and visualization charts.

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• Examining estimated purchase cost distributions for Chinese A-share stocks
• Checking the estimated share of holdings below the current price
• Identifying price ranges with concentrated holdings

Retrieve financial data through natural language queries, including Chinese A-share stocks, funds, futures, company financials, and economic indicators. The skill calls a Python script and presents key results as concise summaries and tables.

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• Checking stock prices and daily market data
• Retrieving recently disclosed revenue and profit
• Comparing prices across stocks

Retrieve locally stored Chinese A-share prices, financial statements, and valuation context through the project's shared interfaces. Look up technical indicators, inspect available data fields, and check data dates when results disagree.

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• Checking a stock's prices and valuation context
• Retrieving financial data across reporting periods
• Looking up technical indicators and their data dates

Investigate and fix agent behavior in TradingAgentsCN by checking active database prompt templates, prompt assembly code, and output processing. Apply targeted changes and verify them with focused tests, template checks, and workflow logs.

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• Investigating prompt changes that do not affect runtime behavior
• Synchronizing database templates with prompt assembly code
• Tracing incorrect dates or price references in position analysis

Search financial news, industry developments, and company sentiment using natural-language questions. Retrieve related headlines, summaries, sources, and article links through a Python script, with optional date ranges to focus the search on a specific period.

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• Finding financial news related to a question
• Researching market and industry developments
• Checking news and public sentiment about a company

Design and generate agents in Nanobot by finding reusable tools, skills, prompts, and existing agent configurations. Guide requirements and confirmation, create focused agent assets, and follow recorded real-data testing and evaluation before publication.

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• Designing an agent around existing project resources
• Adapting existing agent configurations and prompts
• Identifying missing tools and calculation capabilities