StatsClaw: An AI-Collaborative Workflow for Statistical Software Development

• URL: http://arxiv.org/abs/2604.04871v1
• Date: Mon, 06 Apr 2026 17:18:53 GMT
• Title: StatsClaw: An AI-Collaborative Workflow for Statistical Software Development
• Abstract summary: Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation.<n>We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation.<n>Results show that StatsClaw can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.
• Score: 1.8686116192307898
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation -- a critical requirement for statistical software. We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation. A planning agent produces independent specifications for implementation, simulation, and testing, dispatching them to separate agents that cannot see each other's instructions: the builder implements without knowing the ground-truth parameters, the simulator generates data without knowing the algorithm, and the tester validates using deterministic criteria. We describe the approach, demonstrate it end-to-end on a probit estimation package, and evaluate it across three applications to the authors' own R and Python packages. The results show that structured AI-assisted workflows can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.

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