AI Code Review RiskRadar

AI Code Review and Code Risk Review quality gate for Git Diff, release readiness, regression testing, security testing, dependency impact, runtime risk, LLM testing, Agentic AI risk, and RAG security.

RiskRadar AI

RiskRadar AI is a general-purpose code risk review and release-quality gate skill for software teams. It turns code changes into actionable engineering and QA evidence: exact code locations, impact, severity, confidence, test strategy, and release recommendation.

It works for backend, frontend, mobile, platform, data, AI, LLM, Agent, RAG, API, infrastructure, and integration-heavy systems.

When To Use

Use this skill when you need to:

• Review a pull request or Git Diff before release.
• Find functional, runtime, dependency, security, performance, compatibility, and regression risks.
• Review AI-enabled workflows such as LLM calls, agents, RAG retrieval, prompt templates, tool calling, and model fallback logic.
• Build a release quality gate with concrete verification steps.
• Generate unit, integration, E2E, adversarial, resilience, and monitoring test recommendations.

Core Workflow

1. Scope the change: identify repositories, files, Git Diff, affected modules, user flows, APIs, data flows, and runtime boundaries.
2. Map critical paths: trace state transitions, external dependencies, persistence, network calls, async jobs, permissions, and user-visible impact.
3. Identify AI dependencies: detect LLM calls, agents, RAG, prompt templates, model routing, tool calls, feature flags, and fallback behavior when applicable.
4. Analyze risks: classify functional, runtime, dependency, security, performance, AI, privacy, resilience, and regression risks.
5. Validate evidence: link every finding to code, configuration, data flow, logs, tests, or product behavior.
6. Design tests: produce targeted unit, integration, E2E, adversarial, resilience, and monitoring tests.
7. Write the report: include severity, confidence, exact location, impact, verification steps, and release recommendation.

Reference Guides

Topic	Reference	Load When
Code risk review	references/code-risk-review-playbook.md	Always use for risk classification, evidence requirements, and report table

Required Output

Produce a Markdown report with:

• Executive summary and release recommendation.
• Risk table with severity, confidence, exact code location, impact, and fix/test recommendation.
• AI workflow risk section when LLM, Agent, RAG, model, or tool-calling logic is involved.
• Test strategy by layer: unit, integration, E2E, adversarial, resilience, monitoring.
• Evidence checklist and open questions.

AI Workflow Risk Section

## AI Workflow Risk Review

| Risk | Code Location | User/System Impact | AI/Model/Agent Link | Evidence Needed | Verification | Priority |
| --- | --- | --- | --- | --- | --- | --- |

Quality Bar

• Do not invent risks without code or configuration evidence.
• Every high-severity risk must include a concrete reproduction or verification path.
• Separate facts, assumptions, and inferred risks.
• Prefer actionable, release-blocking findings over generic advice.
• If evidence is insufficient, state exactly what is missing.