🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no network calls, shell executions, or obfuscation techniques observed. The metadata suggests a single package from the maintainer, which might warrant further investigation but does not currently indicate malicious intent.

No network calls or shell executions detected.
Low obfuscation and credential risk.
Maintainer has only one package listed.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution detected, indicating no immediate risk from command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities are detected.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/Rul1an/assay/blob/main/README.md
Detailed PyPI description (1871 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 100 commits in Rul1an/assay
Small but multi-author team (3–4 contributors)

🔬 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

Repository Rul1an/assay appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Assay" 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 assay-it

Your task is to develop a Python-based utility named 'PolicyCoverageAnalyzer' which leverages the 'assay-it' package to analyze and visualize policy coverage in software systems. This utility will help developers understand where their policies are being enforced and where they might be missing coverage, aiding in improving security and compliance.

**Core Features:**
1. **Trace Capture**: Implement functionality to capture execution traces of a given program or module. These traces should include details about function calls, return values, and any policy checks performed during execution.
2. **Policy Coverage Analysis**: Analyze the captured traces to determine the coverage of different policies across various functions and modules. Highlight areas where policies are not being checked or are not being enforced properly.
3. **Visualization**: Provide a simple visualization tool that allows users to see the policy coverage as a graph or chart. This could be implemented using a library like matplotlib or seaborn.
4. **Explanation Helpers**: Use the 'assay-it' package's explanation helpers to generate human-readable explanations for why certain policies are or are not being covered. These explanations should be easy to understand and provide actionable insights.
5. **Interactive Mode**: Include an interactive mode where users can query specific functions or modules for policy coverage information. This feature should allow users to explore the data dynamically and get immediate feedback on their queries.

**Utilization of 'assay-it':** 
- Use 'assay-it' to capture execution traces during runtime. Ensure that these traces include all necessary details for analysis, such as function names, input parameters, and results.
- Leverage the policy coverage analysis capabilities provided by 'assay-it' to identify gaps in policy enforcement. This involves understanding how 'assay-it' defines and measures policy coverage.
- Employ 'assay-it's' explanation helpers to create clear, concise explanations for the coverage results. These explanations should be tailored to help developers make informed decisions about their code.
- Integrate 'assay-it' into your visualization tool to ensure that the visual representations accurately reflect the policy coverage data.

Your final deliverable should include a well-documented Python script, along with sample usage scenarios and expected outputs. Additionally, provide a brief report summarizing the key findings from the policy coverage analysis and recommendations for improving policy enforcement in the analyzed system.