corpus-investigation

Corpus investigation uses stratified sampling and shell-based pattern verification across 100GB+ archives to extract metadata schemas and chunking strategies. This token-efficient approach produces a Section Analysis Document with RAG-ready ingestion plans without exhaustive reading of every document.

A 5-phase investigation framework—Reconnaissance, Stratified Sampling, Pattern Verification, Edge Case Analysis, and Synthesis—systematically analyzes mixed formats across thousands of files. Grep and shell tools quantify coverage and patterns while minimizing token usage, outputting structured metadata and RAG guidance.

Yes. Computational pattern verification using grep, find, and similar shell tools quantifies structural coverage and patterns across thousands of files without token-intensive reading, making it practical for corpora exceeding 100GB.

The Section Analysis Document includes a 5-layer metadata schema derived from representative sampling patterns. This schema defines section structure, content types, and chunking boundaries, serving as a blueprint for RAG pipeline configuration and knowledge base design.

Stratified sampling across size, time, type, and depth dimensions selectively represents the corpus landscape with minimal tokens. This approach preserves structural and content patterns while dramatically reducing analysis scope on massive document collections.

No. Token-efficient investigation uses representative sampling and computational pattern verification to infer structure across thousands of files. The Section Analysis Document captures layout, metadata, and chunking guidance without exhaustive document review.