Standard linters (eslint, flake8, cargo check) were built to catch human syntax and style conventions. But when coding with AI agents like Claude Code, Cursor, or Gemini, the problem is different: AI writes code that is syntactically clean, but semantically broken.
Traditional linters pass code that secretly introduces:
- Phantom / hallucinated dependencies that do not exist on npm, PyPI, or crates.io.
- Training-cutoff API drift (methods deprecated, altered, or hallucinated).
- Async starvation, silent thread blocking, and race conditions.
- Web3 / smart contract logic traps (e.g. unchecked account validation, reentrancy).
To solve this, we built Parry — an open-source hybrid code verifier and developer security tool designed to deflect AI hallucinations locally before git push.
The 100-Case Evil AI Benchmark
We verified standard linters against our Top-100 Evil AI Benchmark — 100 real-world defects generated by GPT-4o, Claude, Cursor, and Gemini across Python, TypeScript, JavaScript, Rust, and Solidity:
- Standard linters caught only a tiny fraction of semantic defects because the syntax was valid.
- Parry's sub-15ms AST engine deflected 100 / 100 (100%) with exact rule attribution.
How to Use It
Parry operates with zero style nitpicks as a local CLI, pre-commit hook, or native Model Context Protocol (MCP) server for Claude Code, Cursor, and Gemini.
- Production Workbench & Docs: parry.scopeledger.net
- Open-Source Benchmark & CLI: github.com/AI-Ops1/Parry
Run it against your project:
python parry.py check . Or run the full 100-case suite:
python parry.py bench Would love to hear how other teams and solo developers are gating agent-generated PRs before code lands in production!
