long-document-evidence-reader

Yes, you can retrieve traceable evidence from ultra-long PDFs by loading them into a ContextStore as chunks. The skill uses a Python REPL context and BM25 pre-filtering to search and cite specific sections without overwhelming the LLM context window.

Yes, you can search large codebases by loading the repository into a ContextStore as chunks. Using a Python REPL context, you interactively inspect chunks and execute sub-LLM queries to return final answers with traceable source references.

Yes, BM25 pre-filtering helps long document retrieval by narrowing down candidate chunks before deeper analysis. It identifies the most relevant text segments early, reducing processing overhead and improving the accuracy of evidence extraction from large collections.

Ensure reproducible answers when querying large collections by using a FINAL or FINAL_VAR output protocol. This structured output mechanism maintains evidence references and headers, guaranteeing that results are citable and auditable across multiple runs.

Yes, you need PyPDF or PyPDF2 dependencies installed to load PDFs for evidence extraction. The skill uses the load_pdf loader to process the documents, chunk their content, and make them available for querying within the REPL environment.

The best way to maintain source traceability for compliance documents is chunking content into a ContextStore with reference headers. Using a REPL environment with a FINAL output protocol ensures every extracted answer retains its original citation for audits.