financials-normalizer

To normalize messy public-company financial data, this Skill transforms extracted rows into a standardized long-form schema, preserving source provenance and applying governance rules for citations, period labeling, and currency units to produce auditable outputs.

Financial data normalization with source indexing and QA flags is the process of structuring extracted financial rows into a long-form schema while generating a Source_Index and Normalization_Issues file to ensure traceability and highlight data conflicts for audit trails.

You generate an audit trail for normalized financial statements by running the normalization pipeline, which produces Normalized_Financials_Long.csv alongside Source_Index.csv and QA artifacts that track evidence labels and preserve raw inputs for downstream review.

Yes, you can use normalized financials for downstream equity models and earnings analysis because the Skill outputs a model-ready long-form schema specifically designed to enable downstream workflows like earnings modeling and investment memos.

Yes, this financial normalization process handles currency units and evidence labeling by enforcing governance rules that standardize period labels and evidence types while preserving source citations and generating confidence labeling artifacts for audit.

Normalizing extracted financial rows without QA flags limits auditability, whereas this Skill prevents that constraint by generating Normalization_Issues.csv and preserving raw input conflicts to ensure downstream model loading remains fully traceable and governed.