AI Code Review RiskRadar

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

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.