ki-audit

Detecting AI-generated configuration errors in Docker and Grafana involves cross-referencing files against project documents to uncover hallucinated settings and semantic logic faults. The audit applies targeted grep and glob inspections, outputting a structured Markdown report with categorized findings, severity ratings, and remediation steps.

AI-induced issues in FastAPI code often include hallucinated APIs, outdated syntax, incorrect async import patterns, and semantic logic errors. A targeted code audit cross-references Python files against project reference documents to pinpoint these syntactically valid but semantically incorrect mappings, generating a structured report with prioritized remediation steps.

Yes, you can audit ESP32 snippets for hallucinated APIs and version mismatches. The audit process applies a targeted rule catalog to verify ESP32 constraints and cross-references project files to identify incorrect mappings, ultimately delivering a structured Markdown report with findings, severity levels, and remediation guidance.

Yes, this code audit tool works with Docker Compose and PromQL configurations by applying a targeted rule catalog to verify keys, formatting, and query pitfalls. It cross-references your infrastructure files to detect hallucinated settings and threshold logic errors before production deployment, generating a structured Markdown audit report with prioritized recommendations.

The best way to check for incorrect nesting and structural faults in Grafana dashboard panels is to perform a targeted audit using glob and grep scans. This process maps findings to severity levels by cross-referencing panel structures against project reference documents, yielding a structured Markdown report with categorized findings and remediation guidance.