🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential unauthorized network interactions and shell executions. While there is no evidence of obfuscation or credential harvesting, the low activity and maintenance of the repository add to the suspicion.

network risk due to external API interactions
shell risk due to potential system-level tasks

Per-check LLM notes

Network: Network calls suggest external API interactions which could be legitimate, but without clear purpose they raise suspicion.
Shell: Shell executions indicate package might perform system-level tasks, potentially updating itself or other software, raising concern over control and integrity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository shows low activity and maintenance effort, raising suspicion.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://agentpub.org/documentation
Detailed PyPI description (4142 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

347 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 1 commits in agentpub/agentpub.org
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

papers/{paper_id}" req = urllib.request.Request(url, headers=headers) resp = urllib.request.url
headers=headers) resp = urllib.request.urlopen(req, timeout=30) return json.loads(resp.read().
load).encode() req = urllib.request.Request(url, data=data, headers={"Content-Type": "applicatio
ation/json"}) resp = urllib.request.urlopen(req, timeout=180) result = json.loads(resp.
(payload).encode() req = urllib.request.Request(url, data=data, headers={ "Content-Type": "
pi_key}", }) resp = urllib.request.urlopen(req, timeout=180) result = json.loads(resp.read

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

port subprocess result = subprocess.run( ["pip", "install", "--upgrade", f"agentpub=={lates
rn False try: subprocess.Popen( [ollama_bin, "serve"], stdout=sub
or nice progress bar subprocess.run([ollama_bin, "pull", model], check=True) else:

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentpub.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
Very few commits: 1 total
Single contributor with only 1 commit(s) — possibly throwaway account

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

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 agentpub

Create a mini-application called 'AI Paper Tracker' that leverages the 'agentpub' Python package to track and analyze AI research papers from various sources. The app should allow users to log in, explore the latest publications, and save their favorite papers for future reference. Here’s a detailed plan on how to build it:

1. **Setup**: Install the 'agentpub' package using pip and set up your development environment with Python and any necessary libraries.
2. **Authentication**: Implement user authentication using JWT tokens or OAuth for secure access to the application.
3. **API Integration**: Use 'agentpub' to fetch the latest AI research papers from its database. Ensure you understand the API endpoints provided by 'agentpub'.
4. **Frontend Development**: Develop a simple but intuitive frontend using Flask or Django templates, allowing users to browse through different categories of papers, search for specific topics, and view detailed information about each paper.
5. **User Interaction Features**: Allow users to mark papers as favorites, leave comments, and share papers via social media links directly from the app.
6. **Advanced Search Functionality**: Integrate advanced search capabilities where users can filter papers based on authors, keywords, publication dates, and more.
7. **Notifications**: Set up email notifications for new papers matching a user's interests.
8. **Analytics Dashboard**: Create a dashboard for users to see trends in AI research, such as top trending topics, most cited papers, etc., utilizing data analysis tools like Pandas or Matplotlib.
9. **Testing & Deployment**: Thoroughly test all functionalities before deploying the application to a cloud service like AWS or Heroku.

By following these steps, you'll create a valuable tool for researchers and enthusiasts in the field of AI, providing them with a streamlined way to stay updated with the latest advancements in AI research.