Analysis of 559 AI coding rule files finds 63% of content is not instructions

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A developer building a tool to verify AI coding agent compliance analyzed 559 public configuration files — including CLAUDE.md, AGENTS.md, and Copilot instructions — from projects like PyTorch, Kubernetes, and Elasticsearch. Parsing over 23,700 items revealed that 62.9% of content in these files is documentation such as directory listings, architecture notes, and reference tables rather than actual directives. Of the genuine rules identified, only 43.5% are mechanically verifiable, such as 'never commit to main,' while the remaining 56.5% involve subjective behaviors like 'surface bad news first' that require human judgment to assess. The 63/37 split between non-instructions and rules remained largely stable as the corpus grew from 40 to 559 files across different formats, suggesting the distinction is a property of language rather than file structure. The developer has released a free, locally-run open-source tool called RuleReceipt to help users audit their AI agent rule files.

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ProgrammingDEV Community ·

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