ai-change-risk-reviewer

name	ai-change-risk-reviewer
description	Review AI-assisted changes before execution for automation boundaries, human approval, affected-system criticality, and audit evidence.
version	1.0.0
since	2026-08-27
last_modified	2026-08-27
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":"2026-08-27","change":"Initial generated production-ready SDLC / DevSecOps skill"}]

Ai Change Risk Reviewer

Purpose

Review AI-assisted changes before execution: automation boundaries, human approval, affected-system criticality, and audit evidence. 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

• AI-assisted change risk decisions, controls, or operating practices need independent review.
• A change affects AI-assisted change risk artifacts such as AI change proposal, automation boundary, approval record, system criticality, execution plan, audit trail.
• The user needs evidence-oriented findings for risks such as unapproved autonomous change, critical-system impact, ambiguous human oversight, untracked AI rationale, unsafe tool use, missing 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 AI-assisted change risk artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as human approval, change classification, tool call log, diff summary, validation result, rollback plan.
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.