🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks associated with network and shell activities, suggesting potential unauthorized actions. While there is no concrete evidence of malicious intent, the combination of these risks and the incomplete metadata raise concerns about its safety.

High network risk due to external calls
Significant shell risk from executing commands

Per-check LLM notes

Network: The package makes external network calls which could potentially be used for unauthorized data transfer.
Shell: Executing shell commands can lead to system-level access and manipulation, posing a significant risk.
Obfuscation: The use of base64 decoding suggests some level of obfuscation, but it's not conclusive without more context on the purpose.
Credentials: No clear patterns indicating credential harvesting were found.
Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some suspicion but not enough to conclusively determine malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

) response = requests.get(api_url, timeout=10) if response.status_code == 200
Crew/tags" response = requests.get(tags_url, timeout=10) if response.status_code == 20
pfile response = requests.get(config_uri, timeout=30) response.raise_for_statu
tion...") resp = requests.post( "https://github.com/login/device/code",
ep(5) resp = requests.post( "https://github.com/login/oauth/access_

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

"file_data": base64.b64decode(file_data.bytes), "file_name": file_

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

date command result = subprocess.run(command, shell=True, capture_output=True, text=True)
ult = subprocess.run(command, shell=True, capture_output=True, text=True) if result.returnc

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: saigontechnology.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository saigontechnology/AgentCrew appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 agentcrew-ai

Create a collaborative storytelling app using the 'agentcrew-ai' package. This app will allow multiple users to contribute to a shared story in real-time, each adding their own unique perspective or character. The core functionality of the app includes:

1. User Authentication: Users must be able to sign up and log in to the app.
2. Real-Time Collaboration: The story should update in real-time as users add new content.
3. Role-Based Contributions: Users can choose to contribute as different characters or perspectives within the story.
4. AI Integration: Utilize 'agentcrew-ai' to generate responses from the AI based on user inputs, enhancing the narrative flow and providing creative suggestions.
5. Story History: Maintain a history of all contributions made to the story.
6. Voting System: Allow users to vote on the best contributions or directions for the story.
7. Analytics Dashboard: Provide an analytics dashboard showing user engagement, contribution frequency, and popular characters/perspectives.

How to Use 'agentcrew-ai':
- Integrate 'agentcrew-ai' to handle the AI-driven narrative enhancements, such as suggesting plot twists, character development ideas, or thematic elements.
- Use the package to analyze user inputs and generate relevant responses or prompts to guide the storytelling process.
- Implement a feature where the AI can suggest alternative endings or paths based on user contributions, encouraging creativity and exploration.