🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minor concerns about obfuscation and metadata. There is no strong evidence of malicious activity.

Moderate obfuscation risk due to base64 encoding
Lack of detailed metadata and PyPI classifiers

Per-check LLM notes

Network: The observed network call patterns are typical for fetching resources or making API calls, which could be legitimate depending on the package's functionality.
Shell: No shell execution patterns were detected, indicating no immediate risk associated with executing arbitrary commands.
Obfuscation: The usage of base64 encoding and decoding suggests some level of obfuscation, but it may also be used for legitimate purposes such as data encryption or transmission.
Credentials: No clear patterns indicating credential harvesting were detected.
Metadata: The package shows some red flags such as a single package from the author and lack of PyPI classifiers, but no direct evidence of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

drock: async with httpx.AsyncClient() as client: response = await client.get(url
else: async with httpx.AsyncClient() as client: response = await client.get(url
eturn url async with httpx.AsyncClient() as client: response = await client.get(url)

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

image_bytes = base64.b64decode(base64_string) else: raise Value
ata, str): return base64.b64decode(data.encode("utf-8")) return data def _build_wa
byte_count = len(base64.b64decode(data.encode("utf-8"))) else: byte_count

✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:25751/tracer/`.

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "PrismShadow" 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 agenthub-python

Create a Python-based chatbot application named 'AgentAssistant' using the 'agenthub-python' package. This application will serve as a personal assistant capable of understanding natural language commands and executing tasks accordingly. It will leverage the precision and autonomy provided by the AgentHub platform to handle a wide array of user requests efficiently.

**Features:**
1. **Task Execution:** Users can ask the chatbot to perform various tasks such as setting reminders, scheduling meetings, sending emails, and more.
2. **Contextual Understanding:** The chatbot should be able to understand the context of conversations, remembering previous interactions and using them to better assist the user.
3. **Customization Options:** Allow users to customize their experience by setting preferences for notifications, task priorities, and more.
4. **Integration Capabilities:** The chatbot should integrate with external services like calendar apps, email clients, and other APIs.
5. **Learning and Improvement:** Implement a feature where the chatbot learns from user interactions to improve its responses and task execution over time.

**Steps to Build the Application:**
1. **Setup Environment:** Install Python and necessary libraries including 'agenthub-python'.
2. **Design User Interface:** Create a simple and intuitive UI where users can interact with the chatbot.
3. **Integrate 'agenthub-python':** Use the 'agenthub-python' package to connect to the AgentHub platform and enable the chatbot's autonomous capabilities.
4. **Develop Core Functions:** Write functions that allow the chatbot to execute common tasks based on user commands.
5. **Implement Contextual Memory:** Develop a system to store and recall contextual information from previous conversations.
6. **Add Customization Features:** Provide options for users to tailor their chatbot experience.
7. **Connect External Services:** Integrate the chatbot with third-party services to expand its functionality.
8. **Incorporate Learning Mechanism:** Include a feedback loop where the chatbot's performance improves with each interaction.
9. **Testing and Debugging:** Thoroughly test the application to ensure it works as expected and fix any bugs.
10. **Deployment:** Once tested, deploy the application so users can access it via web or mobile interfaces.