🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to its current state of active development and lack of transparency in metadata, despite showing low risks in other areas such as shell execution and obfuscation.

Moderate network risk
Low effort and potential lack of transparency in metadata

Per-check LLM notes

Network: The presence of network calls suggests the package interacts with external services, which could be legitimate but requires further investigation to ensure it's not misused.
Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and potential lack of transparency, increasing suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: resp = requests.post(url, headers=self.headers, json=payload) data =
rd pattern resp = requests.post(url, headers=self.headers, json=payload) data =

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Package is very new: uploaded 2 day(s) ago
Author name is missing or very short
Author "" 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 agent-assembler

Create a command-line utility named 'ContextualAgent' using Python and the 'agent-assembler' package. This utility will serve as a platform for managing and orchestrating AI agents in a deterministic manner. Your goal is to develop a tool that allows users to define and manage complex workflows involving multiple AI agents, ensuring that each agent operates within a specific context and that the overall workflow remains predictable and repeatable.

### Core Features:
1. **Agent Definition**: Users should be able to define individual agents with unique identifiers, roles, and specific tasks. Each agent must have a defined context and parameters that dictate its behavior.
2. **Workflow Creation**: Allow users to create workflows by assembling these agents into sequences or parallel structures. Workflows should be deterministic, meaning that given the same input, they produce the same output every time.
3. **Context Management**: Implement a feature that allows the dynamic modification of contexts for agents based on external events or conditions. This ensures that agents can adapt their behavior without altering their core definitions.
4. **Execution Control**: Provide functionality to start, pause, resume, and terminate workflows. Include logging and error handling to ensure robustness.
5. **Integration with External Services**: Enable the integration of external services or APIs as part of the workflow execution. This could include data retrieval, notifications, or other forms of interaction.

### Utilizing 'agent-assembler':
- Use 'agent-assembler' to define and manage the contexts for each agent. Ensure that the assembly of agents into workflows is done deterministically, leveraging the package's capabilities for context assembly.
- Leverage 'agent-assembler' to handle the state management and transitions between different stages of the workflow execution.
- Explore how 'agent-assembler' can facilitate the creation of reusable agent components and workflows, promoting modularity and ease of maintenance.

### Additional Considerations:
- Design the CLI interface to be user-friendly and intuitive, providing clear instructions and feedback.
- Ensure that the application is well-documented, including setup instructions, examples, and best practices for defining and executing workflows.
- Aim for a clean architecture that separates concerns clearly, making the codebase easy to extend and modify.