🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all major categories with only metadata indicating some signs of low maintenance. However, there are no clear indications of malicious intent or activity.

Low network and shell execution risks
No evidence of obfuscation or credential harvesting
Metadata suggests low maintenance effort

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external communications.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The package shows some signs of low maintenance and effort, but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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 4.0

2 maintainer concern(s) found

Author "Agent Control Team" 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-control-evaluators

Create a command-line utility called 'AgentEvaluator' using Python, which leverages the 'agent-control-evaluators' package to assess the performance of different AI agents in various tasks. This tool will enable users to define evaluation scenarios and then run these scenarios against multiple AI agents, outputting detailed performance metrics.

**Step 1:** Begin by installing the necessary packages including 'agent-control-evaluators'. Ensure your environment is set up correctly to support the latest Python version.

**Step 2:** Design a simple but flexible command-line interface (CLI) where users can specify:
- The type of evaluation scenario they wish to run (e.g., text summarization, question answering).
- Which AI agents they want to test.
- Any specific parameters for the evaluation (such as input data files).

**Step 3:** Implement the core functionality of 'AgentEvaluator' to load the specified AI agents and evaluation scenarios from the 'agent-control-evaluators' package. Utilize the package's built-in evaluators to execute these scenarios against the agents and collect performance data.

**Step 4:** Develop reporting capabilities within 'AgentEvaluator' to display comprehensive results of each test run. These reports should include comparative analysis of different agents' performances across various metrics provided by the 'agent-control-evaluators' package.

**Suggested Features:**
- Support for custom evaluation scenarios beyond those included in 'agent-control-evaluators'. Users should be able to extend the tool's functionality by adding their own scenarios.
- An option for users to save the results of evaluation runs to a file for later review or further analysis.
- Integration with popular machine learning frameworks to allow testing of agents implemented in these frameworks.
- A scoring system that ranks the tested agents based on their performance in each scenario.

**How 'agent-control-evaluators' is utilized:**
- Import evaluators from the package to define the criteria for assessing AI agent performance.
- Use these evaluators to run tests on the selected agents and gather quantitative data about their effectiveness in given tasks.
- Leverage any additional utilities provided by the package to streamline the evaluation process and improve the accuracy of performance metrics.