mlops-governance-reviewer

name	mlops-governance-reviewer
description	Review model versioning, training data, bias, drift, monitoring, approvals, reproducibility, model registry, and deployment gates.
version	1.0.0
since	2026-07-28
last_modified	2026-07-28
authors	["platform-engineering"]
stability	stable
min_platform_version	{"codex":"unknown","amazon-q":"unknown","antigravity":"unknown","auggie":"unknown","bob":"unknown","claude-code":"unknown","cline":"unknown","codebuddy":"unknown","continue":"unknown","costrict":"unknown","crush":"unknown","github-copilot":"unknown","gitlab-duo":"unknown","factory":"unknown","forgecode":"unknown","opencode":"unknown","openhands":"unknown","cursor":"unknown","roo-code":"unknown","kiro":"unknown","junie":"unknown","gemini-cli":"unknown","iflow":"unknown","kilocode":"unknown","kimi":"unknown","lingma":"unknown","pi":"unknown","qoder":"unknown","qwen":"unknown","windsurf":"unknown","ollama":"unknown"}
deprecated_since	null
replaces	null
supersedes	[]
changelog	[{"version":"1.0.0","date":"2026-07-28","change":"Initial generated production-ready SDLC / DevSecOps skill"}]

Mlops Governance Reviewer

Purpose

Review model versioning, training data, bias, drift, monitoring, approvals, reproducibility, model registry, and deployment gates. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.

When to use

• MLOps governance decisions, controls, or operating practices need independent review.
• A change affects MLOps governance artifacts such as model registry entry, training dataset, feature pipeline, bias evaluation, drift monitor, deployment gate.
• The user needs evidence-oriented findings for risks such as unreproducible model, biased dataset, silent drift, unapproved promotion, missing lineage, weak rollback.
• Audit, security, operations, or platform stakeholders need a concise readiness position.
• Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.

Operating model

1. Identify the relevant MLOps governance artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as model version, dataset hash, evaluation report, approval record, serving metric, registry transition.
3. Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
4. Rate findings by operational, security, compliance, customer, and auditability impact.
5. Recommend minimal remediation steps, validation evidence, owners, and review cadence.

Spec-Driven Change Context

• Treat repository specs, ADRs, runbooks, change proposals, design notes, and task files as durable context that outlives a chat session.
• For non-trivial changes, prefer a checked-in change artifact or equivalent proposal/design/tasks record before implementation begins.
• Capture requirement deltas explicitly: added, modified, removed, deprecated, or unchanged behavior.
• Keep implementation tasks traceable to acceptance criteria, affected specs, validation commands, and owners.
• During verification, compare the implementation against the proposal, design decisions, task checklist, and spec deltas.
• After completion, sync or archive completed change artifacts so the repository's source of truth reflects the final behavior.
• If the repository has no spec workflow yet, report the missing artifact and provide a minimal proposal/spec/tasks outline instead of relying on chat-only intent.

Skill-Specific Review Scope

• Primary artifacts: model registry entry, training dataset, feature pipeline, bias evaluation, drift monitor, deployment gate.
• Risk themes: unreproducible model, biased dataset, silent drift, unapproved promotion, missing lineage, weak rollback.