🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to its network activity towards a local server, suggesting potential unauthorized data transmission or C2 activity. However, other aspects such as shell execution, obfuscation, and credential risks are minimal.

network risk 8/10
metadata risk 4/10
newly created repository and package

Per-check LLM notes

Network: The network call to a local server suggests potential unauthorized data transmission or command and control (C2) activity.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository and package are newly created, and the maintainer has a limited history with PyPI, which raises some suspicion but not enough to conclusively determine malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

alse}).encode() request = urllib.request.Request( "http://127.0.0.1:11434/api/generate",
, ) try: with urllib.request.urlopen(request, timeout=120) as response: body

✓ 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 created very recently: 5 day(s) ago (2026-06-01T08:15:39Z)

Repository created very recently: 5 day(s) ago (2026-06-01T08:15:39Z)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Package is very new: uploaded 2 day(s) ago
Author "Osmane B." 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 agent-context-md

Develop a mini-application named 'AgentContextGenerator' that leverages the 'agent-context-md' Python package to automatically generate concise and informative AGENTS.md files for various repositories. The application should be designed to read existing README.md files, extract key information, and use it to create tailored AGENTS.md files that highlight specific roles, responsibilities, and guidelines for agents or contributors within the repository context.

Step-by-Step Development:
1. Set up a virtual environment and install the 'agent-context-md' package along with any other necessary dependencies.
2. Create a user-friendly command-line interface (CLI) for interacting with the application.
3. Implement a function that reads the content of a given README.md file from a local directory or URL.
4. Use the 'agent-context-md' package to process the extracted information and generate AGENTS.md content.
5. Integrate functionality to save the generated AGENTS.md file back into the same directory as the original README.md file or another specified location.
6. Add error handling to manage issues such as missing files or incorrect input formats.
7. Include options for customization, allowing users to specify certain sections or details they want emphasized in the AGENTS.md file.
8. Test the application thoroughly with different types of README.md files to ensure consistency and accuracy in AGENTS.md generation.
9. Document the codebase and provide clear instructions on how to install and run the application.

Suggested Features:
- Ability to process multiple README.md files at once.
- Option to exclude certain sections of the README.md from being included in the AGENTS.md file.
- Support for both local file paths and remote URLs for README.md files.
- Customizable templates for AGENTS.md to allow for personalized formatting and content.
- Detailed logging to track the processing and generation steps.

Utilization of 'agent-context-md':
- Utilize the 'agent-context-md' package's functions to analyze and summarize the essential information from the README.md file.
- Leverage the package's capabilities to avoid redundancy by ensuring that the generated AGENTS.md file does not repeat information already present in the README.md.
- Employ the package's flexibility to tailor the AGENTS.md content based on the specific requirements and context of each repository.