Rule engine performs deterministic static scan, LLM does semantic deep analysis, cross-validation reduces false positives. Outputs 5-dimension scores and directly applicable fix code.
A single rule engine only finds known patterns; a single LLM may miss issues. Dual-engine cross-validation balances determinism and depth.
22 cross-language rules covering security vulnerabilities, performance anti-patterns, AI hallucination patterns. Millisecond response, zero API cost, offline capable.
Receives rule pre-scan results then does semantic review: logic errors, architecture flaws, business boundaries. Confirms or denies rule hits, eliminating false positives.
Each issue is tagged with source: rule (engine only), llm (LLM only), confirmed (both agree). Confidence auto-boosted.
Correctness, Security, Performance, Maintainability, Best Practice — each 0–100. Composite score weighted: security 30% + correctness 25%.
Each issue includes a fix_code snippet, copy-paste ready. Rule engine auto-generates for 8 key rule types, LLM covers complex scenarios.
7 MCP tools for Agent calls, REST API for integration. Remote /mcp endpoint and /v1/* coexist on the same server.
Paste code or diff, click review. Or use CLI: python cli.py --commit HEAD~1
REST API and MCP tools coexist on the same server, ready to use after deployment.