CC-Canary: Detect early signs of regressions in Claude Code
CC-Canary is a drift-detection tool for Claude Code that analyzes local session logs to identify performance regressions and behavioral changes in AI-assisted development workflows, enabling IT organizations to monitor code quality and model reliability without external dependencies or telemetry. By providing early-warning forensic reports on metrics like read-edit ratios, reasoning loops, and token efficiency, the tool helps technology leaders understand whether productivity gains from AI coding assistants are sustaining or degrading over time. This capability is critical for managing AI-assisted development at scale, ensuring compliance with local-only data processing requirements, and making informed decisions about tool adoption and version upgrades.
CC-Canary is a drift-detection tool for Claude Code that analyzes local session logs to identify performance regressions and behavioral changes in AI-assisted development workflows, enabling IT organizations to monitor code quality and model reliability without external dependencies or telemetry. By providing early-warning forensic reports on metrics like read-edit ratios, reasoning loops, and token efficiency, the tool helps technology leaders understand whether productivity gains from AI coding assistants are sustaining or degrading over time. This capability is critical for managing AI-assisted development at scale, ensuring compliance with local-only data processing requirements, and making informed decisions about tool adoption and version upgrades.