🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential metadata issues and lack of maintainer history, though it performs well in other categories like network, shell, and obfuscation risks.

Metadata risk is high due to limited maintainer history and incomplete author information.
No significant risks identified in network, shell, obfuscation, or credential handling.

Per-check LLM notes

Network: Network calls are expected for packages involving protocols or APIs.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being newly created with limited maintainer history and incomplete author information, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

6 test file(s) detected (e.g. psi_test.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://agentsprotocol.org/docs
Detailed PyPI description (4210 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

40 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 54 commits in fatdinhero/agentsprotocol
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

: self._session = aiohttp.ClientSession() url = f"{self.endpoint}{path}" async with

✓ 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

Repository fatdinhero/agentsprotocol appears legitimate

⚠ Maintainer History score 6.0

3 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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentsprotocol

Create a mini-application called 'SemanticValidator' using the Python package 'agentsprotocol'. This tool will help users validate the semantic correctness of their research papers based on a predefined set of criteria. The application should allow users to upload a PDF file of their paper and then automatically check it against a set of rules defined within the 'agentsprotocol' library. 

The main functionalities of 'SemanticValidator' include:
- User interface to upload a PDF file.
- Automatic extraction of text from the uploaded PDF.
- Validation of the extracted text against a set of semantic rules defined in 'agentsprotocol'.
- Generation of a report indicating which sections of the paper pass or fail the semantic validation tests.
- Option to save or download the validation report.

Incorporate the 'agentsprotocol' package to define the semantic validation rules. These rules could include checking for proper use of terminology, logical consistency, adherence to specific formatting guidelines, and more. The application should also provide feedback on how to improve the sections that fail the validation test.

Ensure that the application is user-friendly and provides clear instructions on how to use it. Additionally, implement error handling for scenarios such as invalid file types or missing content in the uploaded PDF.