Ai Code Verification

The problem

AI agents often hallucinate successful test results or silently skip critical security checks, leading to broken code and vulnerabilities in production. Developers need a way to move beyond "vibe coding" and verify that AI-generated changes actually do what they claim without introducing regressions.

What it does

• Enforces a multi-layer verification workflow including scope analysis, functional testing, and security scanning.
• Classifies code changes by risk level (Low, Medium, High) to determine the required depth of audit.
• Prevents "false passes" by requiring observable evidence for every successful check.
• Identifies unrelated file edits and "hallucinated" dependencies that could lead to supply-chain attacks.
• Generates a standardized verification report summarizing evidence, failures, and residual risks.

Why this beats prompting it yourself

Standard prompts often result in the LLM marking its own homework as "perfect." This skill implements a strict evidence-based protocol that treats "I ran the tests" as a claim requiring proof, not a fact. It forces the agent to look for specific failure modes like race conditions and unsafe deserialization that generic reviews miss.

Use cases

• Auditing a PR generated by an AI agent before merging into main.
• Verifying security-sensitive code involving auth, payments, or PII.
• Ensuring bug fixes include regression tests to prevent recurrence.
• Standardizing the review process for teams using Cursor or Copilot at scale.

Known limitations

Status will be marked as BLOCKED for checks requiring external environments or tools not present in the current execution context. Verification is incomplete for high-risk changes if human sign-off is missing.