| 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 | 2026-08-29 |
| last_modified | 2026-08-29 |
| 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":"2026-08-29","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.
Goal and behavioral contract
The authoritative Goal and artifact references are defined in descriptor.yaml. Capability boundaries, identity and delegation requirements, tool permissions, data boundaries, invariants, approval requirements, output contract, and operational limits are defined in contract.yaml. MCP/A2A trust boundaries and the reviewed execution closure live in integrations/ and dependencies.yaml; ASPS and assurance requirements live in assurance.yaml.
Treat those declarations as mandatory execution constraints. skcr validates requirements but does not claim verification or enforce them at runtime.
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
- Identify the relevant MLOps governance artifacts, owners, systems, environments, and review boundary.
- Compare the available artifacts against expected signals such as model version, dataset hash, evaluation report, approval record, serving metric, registry transition.
- Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
- Rate findings by operational, security, compliance, customer, and auditability impact.
- 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.
