🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network activity and unusual commit patterns, raising concerns about potential unauthorized data transmission and suspicious development practices.

Moderate network risk due to external URL calls
Unusual metadata risk indicated by rapid commits and low repository activity

Per-check LLM notes

Network: The presence of network calls to an external URL might indicate legitimate functionality like updating or fetching resources, but could also suggest unauthorized data transmission.
Shell: No shell execution patterns detected, suggesting the package does not directly execute system commands which reduces immediate risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The rapid commits and low repository activity suggest potential suspicious behavior.

📦 Package Quality Overall: Low (3.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4038 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

7 type-annotated function signatures (partial)

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 18 commits in spanigrahidev/aillmcleaner
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

} response = requests.post(OLLAMA_URL, json=payload, timeout=60) if respons

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
All 18 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Sujoy Panigrahi" 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 aillmcleaner

Create a Python-based desktop application named 'DataSanitizer' that leverages the 'aillmcleaner' package to clean and sanitize user-provided datasets before they are saved or shared. This application should be designed to handle common data quality issues such as missing values, inconsistent formatting, and redundant entries. Here are the key steps and features for your project:

1. **User Interface**: Develop a simple, intuitive GUI where users can upload their dataset in CSV format. Provide options for users to preview the data and select columns for cleaning.
2. **Data Preview**: Allow users to view the first few rows of the uploaded dataset directly within the application.
3. **Cleaning Options**: Offer a variety of cleaning operations including handling missing values, standardizing formats (e.g., dates, addresses), removing duplicates, and correcting spelling errors.
4. **AI-Powered Cleaning**: Utilize 'aillmcleaner' to perform context-aware cleaning. For example, if a column contains dates but the format is inconsistent, 'aillmcleaner' should automatically detect and correct these inconsistencies based on the context of other date entries in the same column.
5. **Preview Cleaned Data**: After applying selected cleaning operations, display the cleaned version of the dataset so users can review the changes.
6. **Save Cleaned Data**: Provide functionality for users to save the cleaned dataset either back into the original file or as a new file.
7. **Help and Documentation**: Include a help section explaining each cleaning operation and how 'aillmcleaner' works under the hood to ensure users understand the benefits of AI-driven data cleaning.
8. **Error Handling and Feedback**: Implement robust error handling to manage issues like unsupported file formats or invalid data types. Inform users about any issues encountered during the cleaning process and suggest possible solutions.

Utilize 'aillmcleaner' throughout the data cleaning process to ensure that the cleaning actions are not only effective but also contextually appropriate, enhancing the overall quality and usability of the cleaned data.