🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to significant metadata issues, despite showing no signs of immediate malicious activity such as network calls or shell executions.

Lack of maintainer history
Missing author details
Low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no direct system command execution risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several red flags including lack of maintainer history, missing author details, and low metadata quality, suggesting potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentir-langgraph

Create a Python-based language analysis tool that leverages the 'agentir-langgraph' package to perform advanced linguistic operations and generate insightful reports. This tool will serve as a mini-application for analyzing text data, providing features such as sentiment analysis, keyword extraction, and dependency parsing. The application should also include functionality for visualizing the parsed data through graphs and charts, making it easier to understand complex linguistic structures.

The application will consist of three main components:
1. A module for importing and preprocessing text data.
2. A processing engine that uses 'agentir-langgraph' to analyze the text, including sentiment scoring, keyword extraction, and dependency parsing.
3. A visualization module that generates graphical representations of the analyzed data, allowing users to explore linguistic patterns visually.

Suggested Features:
- Sentiment Analysis: Determine the overall sentiment of the text (positive, negative, neutral).
- Keyword Extraction: Identify and rank key phrases within the text.
- Dependency Parsing: Break down sentences into their constituent parts to understand grammatical relationships.
- Graph Visualization: Display dependency trees and keyword distributions as interactive graphs.

Utilization of 'agentir-langgraph':
- Use 'agentir-langgraph' decorators to streamline the development process by automatically handling common tasks such as error checking and logging.
- Leverage the contract tooling provided by 'agentir-langgraph' to ensure that the input data meets the expected format and quality standards before processing.
- Implement client logging helpers from 'agentir-langgraph' to track the performance and usage of the application, aiding in future optimizations and troubleshooting.

Your task is to design and implement this application, ensuring that each component integrates seamlessly with 'agentir-langgraph'. The final product should be user-friendly, efficient, and capable of providing deep insights into any given text data.