🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged due to the high obfuscation risk from the use of 'eval', which can lead to code injection vulnerabilities. Additionally, the missing repository and author details add to the suspicion.

High obfuscation risk due to 'eval'
Missing repository and author details

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell executions detected, indicating no immediate risk of command execution.
Obfuscation: The use of 'eval' with untrusted input is highly suspicious and indicates potential code injection vulnerabilities.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The missing repository and author details raise concerns, but there's no direct evidence of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

self.agent_class = eval(an_agent) self.agents_by_type[self.agent_typ
self.agent_class = eval(an_agent) except NameError: print("class
self.value = eval(self.dist) except Exception as e:
self.observer_class = eval(an_observer) except NameError: p
self.scenario_class = eval(a_scenario) self.new_scenario = self.scenari
self.schedule_class = eval(a_schedule) except NameError: pr

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ 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 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 EcoSimpy

Create a mini-application that simulates a simple economy using the 'EcoSimpy' package. Your application should model a basic market scenario where producers create goods, consumers buy these goods based on their preferences and budget constraints, and intermediaries like retailers facilitate transactions. The goal is to observe how different factors such as supply, demand, pricing, and consumer behavior influence the overall economy over time.

### Core Features:
1. **Agent Creation**: Define classes for producers, consumers, and retailers as agents within the simulation. Each agent should have attributes relevant to its role, such as production capacity, consumption preferences, and inventory levels.
2. **Market Dynamics**: Implement mechanisms for agents to interact. For instance, producers generate goods according to their capacity; consumers make purchasing decisions based on available goods, prices, and their budget; retailers act as intermediaries, setting prices and managing stock.
3. **Simulation Parameters**: Allow users to adjust parameters such as initial market size (number of agents), initial capital distribution, and starting supply levels to see how changes impact the simulation outcomes.
4. **Visualization**: Integrate a visualization component to graphically represent key metrics over time, such as total market value, average prices, and consumer satisfaction levels.
5. **Scenario Testing**: Provide functionality to run multiple simulations with varying conditions to explore the robustness of the economic model under different scenarios.

### Utilization of 'EcoSimpy':
- Use 'EcoSimpy' to structure your simulation framework, leveraging its ABM (Agent-Based Modeling) capabilities to manage interactions between different types of agents.
- Leverage 'EcoSimpy's event scheduling and environment setup functions to handle the timing and conditions under which agents make decisions and interact with each other.
- Apply 'EcoSimpy's data collection tools to gather and analyze simulation results, helping to understand long-term trends and patterns within the simulated economy.