🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential shell execution and network calls, though these may be benign. However, the low activity from the maintainer and the novelty of the package warrant closer scrutiny.

Moderate shell risk
Potential network dependency

Per-check LLM notes

Network: The network call is likely for health checks but could indicate external dependency management.
Shell: Shell execution suggests the package may perform system-level tasks, which can be legitimate but increases risk if not properly managed.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new and the maintainer has limited activity, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

health" try: with urllib.request.urlopen(url, timeout=2) as response: # noqa: S310

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

try: proc = subprocess.run( command, cwd=str(ru
hermes_home)) proc = subprocess.run( [str(runtime.python), "-c", PROVIDER_PROBE_SCRI
ent in payload["events"]: subprocess.run( base + [ "record",
rue, text=True, ) subprocess.run(base + ["compile"], check=True, capture_output=True, text=Tr
pture_output=True, text=True) subprocess.run(base + ["practice", "--output", os.path.join(ingrain_home, "
t=True, text=True) hydrated = subprocess.run( base + ["hydrate", "--level", "evidence", "--query", pa

✓ 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

Repository aeonik-ai/ingrain appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Aeonik" 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 aeonik-ingrain

Create a mini-application called 'AgentExperienceEnhancer' that leverages the 'aeonik-ingrain' package to improve the decision-making capabilities of autonomous agents in a simulated environment. This application will be designed to train an agent to navigate through a complex maze, avoiding obstacles and reaching the goal as efficiently as possible. The application should include the following steps and features:

1. **Setup Environment**: Initialize the maze environment using a simple grid-based system where each cell represents either an open path, an obstacle, or the goal.
2. **Agent Initialization**: Create an autonomous agent capable of moving in four directions (up, down, left, right) within the maze. The agent should have basic movement capabilities without any learning initially.
3. **Incorporate 'aeonik-ingrain'**: Utilize the 'aeonik-ingrain' package to add a learned experience layer to the agent. This layer will allow the agent to learn from its interactions with the environment, improving its decision-making process over time.
4. **Training Phase**: Implement a training phase where the agent navigates through the maze multiple times, learning from its successes and failures. Use the 'aeonik-ingrain' package to update the agent's strategy based on the outcomes of these trials.
5. **Evaluation Phase**: After several rounds of training, evaluate the agent's performance in navigating the maze. Compare its success rate and average time taken to reach the goal before and after the training phase.
6. **Visualization Tool**: Develop a simple visualization tool that allows users to observe the agent's movements and decisions throughout the training process. Highlight the changes in the agent's behavior as it learns.
7. **User Interface**: Create a user-friendly interface that enables users to start, pause, and reset the training process, as well as adjust certain parameters such as the size of the maze or the complexity of the obstacles.
8. **Documentation and Explanation**: Provide comprehensive documentation explaining how the 'aeonik-ingrain' package enhances the agent's learning process and how different aspects of the application work together to achieve better outcomes.

This project aims to demonstrate the power of integrating machine learning into autonomous systems, specifically focusing on how learned experiences can significantly improve performance in dynamic environments.