Extracts the WACC, assumptions, and DCF projections from the markdown metadata file and converts them into a clean, structured JSON file for the frontend interactive viewer.
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This skill reads the completed TICKER_metadata.md file and converts the financial modeling tables into TICKER_financial_model.json.
python skills/model_json_generator/scripts/generate_json.py {TICKER} {TICKER_metadata_path}TICKER_metadata.mdoutput_data/TICKER/TICKER_financial_model.json for consumption by the interactive viewer.还没有评价。欢迎分享你的使用感受。
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Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.
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Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.
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Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.
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Extract balance sheet, income statement, shares outstanding, organic growth, and GAAP reconciliation data from financial PDFs. Standardize via Tiger-Transformer and append structured output to the document markdown.
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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.
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Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.
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