GitHub 仓库中的 AGENTS.md 与 CLAUDE.md 普查：开源项目如何指导编码智能体

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Abstract:Coding agents can be guided with instruction files in a repository. We examine this emerging practice through a census of popular, active GitHub repositories in thirteen languages, treating each repository's root this http URL and this http URL as one instruction set. Agent instructions appear in of repositories and of the most-starred; where both files exist, this http URL usually points to this http URL. We analyse statements using model-assisted annotation and human review. The annotations reveal a practical mix of project knowledge and rules about code, building and workflow. Collaboration rules name concrete requirements, from test plans to commit formats, yet of directives give no reason. Projects disagree on AI attribution: commit-message rules more often prohibit it than require it, while pull-request rules more often require disclosure. A third of instruction sets have a generator-associated signature, a tool-inserted block, shared text or an AI-marked creation commit; four in five are edited after creation. Prespecified tests associate repository popularity with rules on pull requests and AI contributions. Exploratory analyses link activity to process-oriented content and show that LLM and agent projects are over-represented among adopters. The models agree much more closely with each other than with the human reviewer's topic assignments, a caution for automated annotation. We release the annotations, taxonomies and pointers to the analysed file versions.

Subjects:	Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.31715 [cs.SE]
	(or arXiv:2610.31715v1 [cs.SE] for this version)
	https://doi.org/10.48550/arXiv.2610.31715 arXiv-issued DOI via DataCite

Submission history

From: Pawel Zarzycki [view email]
[v1] Thu, 22 Oct 2026 05:44:41 UTC (141 KB)