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CC-Canary: Detect early signs of regressions in Claude Code

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Why This Matters

CC-Canary offers a proactive way for developers and organizations to monitor and detect early signs of regressions in Claude Code, enhancing model reliability without requiring network access or telemetry. This tool helps maintain code quality and performance consistency, which is crucial as AI models become more integrated into software workflows.

Key Takeaways

Drift detection for Claude Code, packaged as two installable Agent Skills. Reads the JSONL session logs Claude Code already writes to ~/.claude/projects/ , detects whether the model has been drifting on your own work, and produces a shareable forensic report.

No network, no account, no telemetry, no background daemon. Runs on the data already on your disk.

Status: 0.x / pre-alpha — output format and metric set may change.

What you get

Skill Invocation Output cc-canary /cc-canary [window] forensic markdown writeup ( ./cc-canary-<date>.md ) — paste-ready for GitHub issues or gists cc-canary-html /cc-canary-html [window] same report as a dark-theme HTML dashboard ( ./cc-canary-<date>.html ), auto-opens in your browser

Window defaults to 60d . Accepts 7d / 14d / 30d / 60d / 90d / 180d .

Each report includes:

Verdict — HOLDING / SUSPECTED REGRESSION / CONFIRMED REGRESSION / INCONCLUSIVE

— HOLDING / SUSPECTED REGRESSION / CONFIRMED REGRESSION / INCONCLUSIVE Headline metrics table (pre vs post, with 🟢/🟡/🔴 band verdicts)

table (pre vs post, with 🟢/🟡/🔴 band verdicts) Weekly trend bars — cost (USD, verified against ccusage to the cent), read:edit ratio, reasoning loops, tokens/turn

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