consistency-auditor

Cross-media consistency auditing checks numerical claims, file references, symbols, and parameters across documents, code, and results to ensure integrity. It compares contents across multiple media types to report mismatches before finalizing a project.

Cross-media consistency auditing supports YAML, MD, JSON, CSV, and binary data formats. It reads multiple file types, including documents and code, to compare contents and verify that all claims and parameters match across the project.

Cross-media consistency auditing validates file references and symbol definitions by reading text files and comparing their contents. It audits decision provenance and parameter values to ensure all definitions remain aligned across documents and code.

Cross-media consistency auditing uses dependencies including pandas, pypdf, and pdfplumber to read and parse multiple file formats. These libraries enable the extraction and comparison of data from documents, code files, and results.

Cross-media consistency auditing checks decision provenance and parameter consistency across multiple media. It audits numbers, file names, and symbols to ensure all assumptions in the paper, code, and results remain consistent before finalization.

Cross-media consistency auditing requires reading multiple file types to compare contents, meaning heavily scanned or unstructured binary formats without extractable text may limit the depth of numerical claims and parameter verification.