🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential for credential harvesting and shell execution, though it does not make network calls or appear obfuscated.

Potential credential harvesting
Legitimate but risky shell execution

Per-check LLM notes

Network: No network calls were detected, reducing immediate risk.
Shell: Shell execution patterns indicate the package runs external commands, which could be legitimate but requires further investigation to ensure commands are not being used maliciously.
Obfuscation: No signs of code obfuscation detected.
Credentials: Potential risk of credential harvesting through suspicious file access patterns.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential low credibility.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

r) -> list[str]: result = subprocess.run( [sys.executable, script], capture_output=Tr
# pragma: no cover return subprocess.run( cmd, check=check, capture_output=ca
parts = editor.split() subprocess.run([*parts, str(path)], check=True) def run_quality_gates() -

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

parse_resource_input("../etc/passwd", default_registry) def test_rejects_inner_dot_dot(sel
parse_resource_input("src/../etc/passwd", default_registry) def test_rejects_dot_segment(self,
ource_input("file://default/../etc/passwd", default_registry) def test_uri_with_multiple_workspa
aim", {"resource": "../etc/passwd", "agent_id": agent_id}, ) assert result["status"]

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository edobusy/agenthold 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 agenthold

Create a mini-application called 'AgentFlowTracker' that leverages the 'agenthold' package to manage shared versioned states among multiple AI agents working on a collaborative task. This application will simulate a scenario where different AI agents are tasked with analyzing data from various sources to predict stock market trends. Each agent will have a specific role such as data collection, preprocessing, model training, and prediction generation.

### Application Requirements:
1. **Setup Environment:** Initialize your Python environment and install necessary packages including 'agenthold'. Ensure all dependencies are clearly documented.
2. **Define Agents:** Create four distinct agents: DataCollector, DataPreprocessor, ModelTrainer, and PredictionGenerator. Each agent should be able to perform its designated task and update the shared state using 'agenthold'.
3. **Shared State Management:** Use 'agenthold' to create a shared, version-controlled state that tracks the progress of each agent. This includes storing intermediate results, errors, and completion statuses.
4. **Version Control:** Implement versioning in the shared state so that changes made by each agent are tracked and can be reverted if needed. This is crucial for maintaining the integrity of the workflow and enabling debugging.
5. **Communication Mechanism:** Develop a simple communication mechanism between agents to ensure they can notify each other about their progress or any issues encountered during execution.
6. **User Interface:** Provide a basic command-line interface (CLI) that allows users to start, stop, and monitor the workflow. Users should be able to see the current state of each agent, the latest version of the shared state, and any errors that occurred.
7. **Testing and Documentation:** Write tests for each component of the application to ensure reliability and provide comprehensive documentation explaining how to set up and use 'AgentFlowTracker'. Include examples of common workflows and troubleshooting tips.

### Features:
- Real-time monitoring of agent status through the CLI.
- Ability to revert to previous versions of the shared state.
- Detailed logs for each agent's activities.
- Scalability to add more agents or modify existing ones easily.
- Clear error handling and notifications.

### Utilization of 'agenthold':
- Use 'agenthold' to initialize the shared state at the beginning of the workflow.
- Each agent updates the shared state after completing its task, ensuring that all changes are versioned.
- Implement a function to query the latest version of the shared state from the CLI.
- Incorporate 'agenthold' into the error handling mechanism to log errors and update the shared state accordingly.
- Provide options in the CLI to revert to a specific version of the shared state.

This project aims to showcase the power of 'agenthold' in managing complex multi-agent workflows and maintaining a consistent, version-controlled state across multiple AI components.