Coding Harness Inspection

Summary

AI coding harnesses are becoming the visible layer between model capability and practical software work. This study treats public repository configuration as a diagnostic signal for that ecosystem: which harnesses developers adopt, how teams configure them, and where usage patterns show early operational maturity.

Scope

The inspection looks across approximately 400,000 public GitHub repositories. The goal is not to measure every private or local workflow, but to build a reproducible thermometer for the public ecosystem around AI coding agents and their supporting harnesses.

What We Track

• Harness-specific configuration files and directories.
• Repository-level setup patterns that indicate active agent use.
• Adoption differences between individual, open-source, and organization-owned repositories.
• Evidence of multi-harness experimentation.

Status

This publication page is ready for the full report body. Add future sections directly in markdown and keep the slug stable at coding-harness-inspection.