🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate obfuscation practices, raising concerns about potential malicious intent or evasion techniques. While there is no clear evidence of credential theft, the maintainer's account status and lack of detail increase suspicion.

High obfuscation risk suggesting possible evasion or misleading analysis
Maintainer's account is new or inactive with limited details

Per-check LLM notes

Obfuscation: The code appears to be obfuscated to potentially hide import checks or other logic, which may indicate an attempt to bypass security measures or mislead analysis.
Credentials: No clear signs of credential harvesting detected.
Metadata: The maintainer has a new or inactive account and lacks detailed author information, which raises some suspicion but does not strongly indicate malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

Test runner config found: pyproject.toml
2 test file(s) detected (e.g. test_template_extractor.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/dreamlx/codeindex
Detailed PyPI description (23602 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

291 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in dreamlx/codeindex
Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

eturn False try: __import__(package_name) return True except ImportError: return

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

500]) try: proc = subprocess.run( ["claude", "-p", prompt, "--model", "haiku",
nic() try: proc = subprocess.run( cmd, cwd=project_dir, capture_output=True, text
ies try: result = subprocess.run( ["codeindex", "affected", "--json"],
ry: scan_result = subprocess.run( ["codeindex", "scan", dir_path, "--quiet"],
updated_readmes: subprocess.run(["git", "add", readme], capture_output=True, timeout=10)
changes diff_result = subprocess.run( ["git", "diff", "--cached", "--quiet"],

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository dreamlx/codeindex appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-codeindex

Your task is to develop a Python-based code search engine called 'CodeSleuth' that leverages the 'ai-codeindex' package to efficiently index and search through large repositories of code. This tool will be particularly useful for developers working on complex projects with extensive codebases, enabling them to quickly find relevant code snippets and understand their context. Here’s a detailed breakdown of what your application should accomplish:

1. **Setup and Installation**: Start by setting up a virtual environment for your project and installing the necessary dependencies, including 'ai-codeindex'. Ensure that you also include other common Python packages like Flask for web serving.

2. **Indexing Mechanism**: Implement a feature within CodeSleuth that allows users to upload a local directory containing source code files (e.g., .py, .js, .java). Use 'ai-codeindex' to create an intelligent index of this codebase. The indexing process should not only catalog each file but also analyze the content to identify functions, classes, comments, and other key elements.

3. **Search Functionality**: Develop a robust search function that allows users to query the indexed codebase using keywords, function/class names, or even partial code snippets. The search results should provide accurate matches along with relevant context such as surrounding lines of code.

4. **User Interface**: Create a simple yet intuitive user interface using Flask to allow users to interact with CodeSleuth. The UI should enable file uploads, keyword searches, and display search results in a readable format.

5. **Advanced Features**: Consider adding advanced features such as code similarity detection (to help identify duplicate code), syntax highlighting in search results, and the ability to save/search personal notes or tags associated with specific code snippets.

6. **Testing and Documentation**: Ensure thorough testing of all functionalities and provide comprehensive documentation detailing how to use CodeSleuth effectively, including setup instructions, usage examples, and troubleshooting tips.

By completing this project, you will have created a powerful tool that simplifies the management and exploration of large codebases, making it easier for developers to navigate complex projects.