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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All organization logic is consolidated into a single script that handles the complete lifecycle: classification parsing, unit harmonization, metadata management, file archival, and input cleanup.
python skills/document_organization/scripts/organize.py {markdown_file}| Step | Action | Details |
|---|---|---|
| 1 | Read Classification | Extracts ticker, company name, document type, dates, currency from the classification table |
| 2 | Harmonize Units | Detects prevailing unit from BS/IS, converts Shares Outstanding and Organic Growth values if they differ. Supports: ones, thousands, ten_thousands (万), millions, hundred_millions (億), billions |
| 3 | Read Financial Summary | Extracts Revenue, EBITA, NOPAT, ROIC, Growth etc. for metadata |
| 4 | Create/Update Metadata | Creates or updates output_data/TICKER/TICKER_metadata.md with document entry + financial history row. Handles deduplication and date sorting |
| 5 | Move & Cleanup | Moves .md and .pdf from processing_data/ → output_data/TICKER/ (removing _temp suffix). Deletes matching source PDF from input_data/ |
| 6 | Verify | Confirms destination files exist and source files are removed |
The script uses a deterministic conversion table:
| Unit | Factor to Ones | Alias |
|---|---|---|
| ones | 1 | — |
| thousands | 1,000 | thousand |
| ten_thousands | 10,000 | 万 |
| millions | 1,000,000 | million |
| hundred_millions | 100,000,000 | 億 |
| billions | 1,000,000,000 | billion |
TICKER_metadata.md with header, processed documents table, and financial history tableDate healing (checking fiscal calendar consistency) is NOT automated by this script. If the agent identifies date inconsistencies after reviewing the metadata, it should flag them manually in the metadata file per the original SKILL instructions (Step 6 of the legacy spec).
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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 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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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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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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