mlops-monitoring-drift

Installation
SKILL.md

Mlops Monitoring Drift

Overview

Use this skill to detect meaningful model degradation early and trigger appropriate remediation actions.

Scope Boundaries

  • Use this skill when the task matches the trigger condition described in description.
  • Do not use this skill when the primary task falls outside this skill's domain.

Shared References

  • Drift alerting and escalation rules:
    • references/drift-alerting-escalation-rules.md

Templates And Assets

  • Drift monitoring policy template:
    • assets/drift-monitoring-policy-template.md

Inputs To Gather

  • Drift signals and quality metrics to monitor.
  • Alert thresholds and acceptable noise level.
  • Escalation owners and response SLA.
  • Retraining and rollback policies.

Deliverables

  • Drift monitoring policy and thresholds.
  • Alert routing and severity model.
  • Response playbook for drift events.

Workflow

  1. Define monitoring policy in assets/drift-monitoring-policy-template.md.
  2. Validate threshold actionability via references/drift-alerting-escalation-rules.md.
  3. Test alert behavior with historical replay or backtests.
  4. Assign response ownership and SLA per severity.
  5. Publish retraining/mitigation decision criteria.

Quality Standard

  • Alerts are actionable, not noise-heavy.
  • Severity levels map to clear response ownership.
  • Retraining triggers are explicit and auditable.

Failure Conditions

  • Stop when drift thresholds are not operationally actionable.
  • Stop when alerts have no clear owner.
  • Escalate when degradation risk remains unmanaged.
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Installs
6
GitHub Stars
5
First Seen
Feb 28, 2026