ln-633-test-value-auditor

What it does

This skill enables AI agents to systematically audit test values within codebases, ensuring data integrity and correctness across automated testing suites. It helps identify discrepancies between expected and actual values to improve test reliability.

When to use it

• Validating input parameters in unit tests against production constraints before deployment.
• Reviewing API response assertions to ensure they match current service behaviors.
• Detecting stale or hardcoded test data that may cause false positives in regression suites.
• Auditing database seed values used for integration testing scenarios.

Key capabilities

• Automated discovery of test value definitions across multiple files.
• Comparison logic to flag mismatches between expected and actual outputs.
• Context-aware analysis linking test values to their source code usage.
• Reporting mechanisms highlighting specific lines with potential data issues.

Example prompts

• "Audit all hardcoded user IDs in the tests/auth directory and check if they exist in the current database schema."
• "Review expected API response codes in this project's integration tests and flag any that no longer match the live endpoint behavior."
• "Identify test cases using deprecated configuration values and suggest updates based on the latest environment variables."

Tips & gotchas

Ensure your AI agent has read access to both the test files and the relevant production code or database schemas for accurate comparison. This skill works best when paired with other validation tools that can execute tests dynamically to verify findings.