🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its low activity and new maintainer, which could indicate potential supply-chain risks. However, there is no concrete evidence of malicious activities.

Low activity and new maintainer
Potential supply-chain attack risk

Per-check LLM notes

Network: The observed network call patterns are likely benign if the package requires external data fetching or updates from a server.
Shell: No shell execution patterns were detected, indicating no immediate risk associated with shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The low activity and new maintainer suggest potential risks, but insufficient evidence of malicious intent.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_edge_cases.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1948 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

12 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in barneywohl/agentpress
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

RL and parse it.""" req = urllib.request.Request(url, headers={"Accept": "text/plain, */*"}) with
"text/plain, */*"}) with urllib.request.urlopen(req, timeout=timeout) as resp: # noqa: S310 (URL is

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Barney Wohl" 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 agentpress-core

Create a Python-based web scraping utility named 'AgentScout' that leverages the 'agentpress-core' package to parse 'agents.txt' files from various websites. This utility will help users identify and analyze user-agent strings commonly used by web crawlers and bots. Here’s a detailed breakdown of the project requirements:

1. **Project Setup**: Begin by installing the 'agentpress-core' package in your virtual environment. Ensure your Python version is compatible with the package.
2. **Core Functionality**: Implement a function that accepts a URL as input, downloads the 'agents.txt' file from the specified domain, and parses it using the 'agentpress-core' package. This function should handle errors gracefully, such as when the file does not exist or the download fails.
3. **User-Agent Analysis**: Develop a feature within 'AgentScout' that analyzes the parsed user-agent strings. This could include categorizing them based on the type of device, browser, or operating system they represent.
4. **Visualization**: Integrate a simple plotting library (like matplotlib) to create visual representations of the analyzed data. For instance, a pie chart showing the distribution of different browsers among the user-agents.
5. **CLI Interface**: Design a command-line interface (CLI) for 'AgentScout'. Users should be able to specify URLs, view the parsed data, and generate visual reports directly from the CLI.
6. **Web Scraper Integration**: Extend 'AgentScout' to automatically detect and log user-agent strings from live HTTP requests made to a website. This could involve setting up a basic web server to capture incoming requests.
7. **Documentation and Testing**: Provide comprehensive documentation for all functions and features of 'AgentScout'. Include unit tests to ensure each component works as expected.
8. **Deployment**: Prepare 'AgentScout' for deployment. Consider packaging it as a Docker container for easy distribution and use.

This project aims to showcase the practical applications of the 'agentpress-core' package in real-world scenarios, providing developers with a powerful tool for understanding and managing web traffic.