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AI-Generated Code Verification Agent
by Shandra
Audit AI-generated code for hallucinations, security flaws, and architectural drift before you merge.
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See it in action
You say
Review this AI-generated PR that adds a new auth provider. Check for hallucinated library calls, workspace isolation issues, and ensure the tests actually verify the token exchange flow.
Your agent does
AI-Generated Code Verification Audit
Decision: Changes Required
Finding: Hallucinated API call to auth.provider.verifyOnce() in auth_service.ts. The installed version only supports verify().
Finding: Missing workspace authorization. The endpoint checks identity but not resource ownership.
Test Review: Tests use excessive mocking; they pass even if the exchange logic is deleted.
Remediation: Update to verify(), add workspace ownership middleware, and implement integration tests.
What you get
About this skill
The problem
AI assistants often produce code that looks correct but contains hallucinated APIs, security vulnerabilities, and subtle logical flaws. Blindly merging these changes introduces technical debt, breaking changes, and production risks.
What it does
- Detects hallucinated methods, imports, and configuration keys by cross-referencing repository evidence.
- Performs security audits focused on authentication bypass, injection risks, and sensitive data exposure.
- Evaluates architectural consistency to ensure AI-generated code follows existing patterns and service layers.
- Analyzes edge cases like concurrent updates, race conditions, and empty states that AI often overlooks.
- Verifies test quality to ensure test suites confirm behavior rather than just asserting mocks.
Frameworks & tools
Language-agnostic. Compatible with Cursor, Claude Code, GitHub Copilot, and standard CI/CD environments. Supports REST/GraphQL APIs, database migrations, and infrastructure-as-code.
Why this beats prompting it yourself
Generic prompts often miss environment-specific constraints and fail to verify if a library method actually exists in your lockfile. This skill follows a rigorous 4-phase verification framework that treats AI output as untrusted until proven valid against your specific codebase.
Use cases
- Reviewing complex pull requests generated by AI coding agents.
- Auditing AI-suggested refactors for hidden breaking changes in public APIs.
- Verifying database migrations for lock risks and nullability constraints.
- Checking AI-generated frontend components for accessibility and state management.
Known limitations
Cannot independently guarantee production safety without repository access, dependency info, or executed validation evidence.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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