🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several risky behaviors including potential network and shell command misuse, as well as obfuscation techniques, raising concerns about its legitimacy and intentions.

High network risk due to POST requests
Potential shell command execution
Use of eval for obfuscation

Per-check LLM notes

Network: POST requests to external URLs with JSON payloads may indicate data transmission that could be used for unintended purposes.
Shell: Executing shell commands related to 'ollama' tool might imply interaction with an external service which could potentially be misused.
Obfuscation: The use of eval with dynamic strings may indicate an attempt to bypass simple code analysis or hide logic, which is often seen in malicious scripts.
Credentials: No clear patterns for harvesting credentials were found, but the absence does not guarantee safety.
Metadata: The repository is not found and the maintainer has a single package, which raises suspicion but does not conclusively indicate malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

8 type-annotated function signatures (partial)

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

} response_maniac = requests.post(url, headers=headers, json=data_maniac) response_ma
} response_psycho = requests.post(url, headers=headers, json=data_psycho) response_ps

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

not None: if eval(f'{self.value} {op} {self.callbacks[keyCB]["Value"]}') and s

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

f.create_modelfile() subprocess.run(["ollama", "rm", self.new_model_name], capture_output=True)
e_output=True) res = subprocess.run(["ollama", "create", self.new_model_name, "-f", self.modelfi

✓ 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "DaarkAngellmc" 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 ai-engine-angelmc

Create a mini-game called 'LifeSim' that simulates the life of AI agents in a simple environment using the 'ai-engine-angelmc' library. This game will allow users to create and manage multiple AI agents that interact with each other and their surroundings in a virtual world. The goal is to observe how these AI agents develop behaviors based on their environment and interactions.

Steps:
1. Install the 'ai-engine-angelmc' package if it's not already installed.
2. Design a basic virtual environment where AI agents can live, move, and interact with objects.
3. Implement the creation of AI agents using 'ai-engine-angelmc'. Each agent should have basic attributes such as health, hunger, and happiness.
4. Program behaviors for the AI agents based on their needs and the environment's conditions. For example, agents might seek food when hungry or avoid danger.
5. Introduce random events into the environment that affect the agents, such as finding food or encountering obstacles.
6. Allow users to control aspects of the environment or directly interact with the AI agents through a simple GUI or command-line interface.
7. Monitor and display the development of the AI agents over time, showing how they adapt and change their behavior based on experiences.

Suggested Features:
- A user-friendly interface for managing AI agents and viewing their status.
- Options to customize the environment, adding more complexity and diversity.
- Logging system to record the history of events and changes in the AI agents' lives.
- Basic AI learning capabilities, allowing agents to improve their strategies based on past experiences.

The 'ai-engine-angelmc' package is utilized throughout the project for generating and controlling the AI agents, providing a framework for their life simulation and interaction within the designed environment.