Purpose
This tool provides structural code intelligence for AI agents. It performs a single scan of your project to build a dependency graph, which can then be queried to find dead exports, circular imports, and usage contexts.

Security Assessment
Overall risk: Low. The tool is written in Rust, which provides strong memory safety. It does not request dangerous system permissions. While there is a rule-based warning regarding environment variable access in a GitHub Actions workflow (`.github/workflows/gemini-dispatch.yml`), this is a standard CI/CD practice for managing pipeline secrets and poses no threat to end-users. A notable security consideration is the installation method: piping a remote shell script directly to `sh` (`curl ... | sh`) is a common but inherently risky pattern. Developers can completely avoid this vector by using the reproducible `cargo install` command or standard package managers like Homebrew instead. No hardcoded secrets were detected in the codebase.

Quality Assessment
Quality is high, and the project is actively maintained (last push was today). It is properly licensed under the permissive MIT and Apache-2.0 licenses, making it safe for commercial and personal use. The only drawback is its low community visibility; with only 7 GitHub stars, it is a very new or niche tool, meaning it has not yet been battle-tested by a large audience.

Verdict
Safe to use, but for optimal security, install via `cargo` or Homebrew rather than the web shell script.