Detect deviations across codebase, canonical memory, and traces with YAML findings.
npx skills add https://github.com/iurykrieger/claude-yoke --skill drift-sense
Please help me install this Agent Skill. Skill: drift-sense Source: https://github.com/iurykrieger/claude-yoke/tree/main/skills/drift-sense Command: npx skills add https://github.com/iurykrieger/claude-yoke --skill drift-sense
Drift-sense provides continuous monitoring to detect deviations across the host codebase, the canonical-memory layer, and historical traces, enabling proactive governance of AI-driven processes.
Invoke drift-sense to scan codebase, canonical memory, and traces and review the generated YAML findings.
High-intent search queries and answers about installing and using this skill.
Drift monitoring identifies memory drift and codebase deviations by comparing current states against historical traces. It emits structured YAML findings to flag misalignment signals for proactive AI governance review before deployment.
Canonical memory drift occurs when AI agent memory diverges from established codebase contracts over time. You need to monitor it in production AI governance workflows to catch early-deprecation signals and recurring contract issues before they cause failures.
Run trace analysis by executing drift-sense with the traces target mode. It scans historical execution traces alongside the codebase and outputs structured YAML findings that highlight deviations requiring canonization review.
Yes, drift-sense integrates with GitHub Actions through the workflow file at .github/workflows/yoke-drift-sense.yml, enabling continuous drift monitoring across codebase, canonical memory, and traces within your CI/CD pipeline.
No, drift-sense operates with zero external dependencies. It analyzes canonical memory, codebase files, and traces natively and emits structured findings directly in YAML format without requiring additional packages.
The best way to format AI governance drift findings is using YAML reports. Drift-sense emits structured YAML findings across codebase, canonical-memory, and trace modes, making outputs machine-readable for automated canonization decisions.
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