🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network activity, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to the package being new, lacking author details, and having an inactive maintainer.

Metadata risk score is high
Package is new and lacks detailed author information
Maintainer seems inactive

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Shell execution appears to be for testing and CLI operations, suggesting normal package behavior.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new, lacks author details, and the maintainer seems inactive, raising suspicion.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 30 test file(s) found

Test runner config found: pyproject.toml
30 test file(s) detected (e.g. test_adapter_scenario_coverage.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (5197 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

452 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

query, ] proc = subprocess.run( # noqa: S603 — trusted OPA call in tests cmd,
="utf-8") completed = subprocess.run( [ opa, "eval",
` runs cleanly.""" proc = subprocess.run( [sys.executable, "-m", "agt.cli", "migrate", "--hel
/ "MIGRATION.md" proc = subprocess.run( [ sys.executable, "-m",
exits non-zero.""" proc = subprocess.run( [ sys.executable, "-m",
tool": None, } proc = subprocess.run( # noqa: S603 — test harness [ "opa", "

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agt-policies

Create a mini-application called 'PolicySimulator' using the 'agt-policies' Python package. This application will serve as a simulation tool for understanding and experimenting with different policies defined within the AGT 5.0 framework over the AGT-vendored ACS engine. The goal is to allow users to input various parameters related to policies and see the outcomes of these policies in a simulated environment.

Step 1: Set up your development environment by installing Python and the 'agt-policies' package. Ensure you have the latest version of the package installed to access all its features.

Step 2: Design a simple user interface where users can select or define their own policies. Policies might include rules around resource allocation, security measures, or operational procedures. Users should be able to specify parameters such as thresholds, conditions, and actions.

Step 3: Implement the core functionality of the 'PolicySimulator'. This involves utilizing the 'agt-policies' package to interpret and apply the selected policies to a predefined set of scenarios. For example, if a policy is about resource allocation, simulate different usage patterns and show how resources are allocated based on the policy.

Step 4: Integrate a visualization component to display the results of applying policies. Use graphs, charts, or other visual aids to clearly illustrate how the policies affect the simulated environment. This could include showing changes over time, comparing multiple policies, or highlighting key metrics like efficiency, cost, or performance.

Step 5: Add an option for users to save and load their policy configurations. This allows them to experiment with different setups without having to redefine everything from scratch each time they use the application.

Suggested Features:
- Support for multiple types of policies (e.g., security, resource management)
- Real-time feedback as policies are adjusted
- Detailed logs of policy effects for analysis
- Comparison tools to evaluate different policy setups side-by-side

By following these steps and incorporating the suggested features, you'll create a valuable tool for anyone interested in exploring the impact of different policies within the AGT 5.0 framework.