🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a low risk score due to no signs of obfuscation or credential harvesting. However, the metadata suggests low effort and potential lack of transparency, raising concerns about its legitimacy.

No obfuscation or credential harvesting detected
Low effort and potential lack of transparency in metadata

Per-check LLM notes

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 package shows signs of low effort and potential lack of transparency, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

15 test file(s) detected (e.g. test_agent.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

24 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 score 3.0

Found 2 network call pattern(s)

try: resp = httpx.post(self._ingest_url, content=body, headers=headers, timeout=10.
one: try: httpx.post(self._legacy_url, json=event, timeout=2.0) except Ex

✓ 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

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)
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 ai-spend-ops

Create a web-based application named 'CostOptimizer' using Python and the 'ai-spend-ops' package. This application will help users manage their cloud service costs across multiple providers such as AWS, Azure, and Google Cloud. The app should allow users to input their cloud usage data and receive recommendations on how to optimize their spending. Here’s a detailed breakdown of the steps and features:

1. **User Authentication**: Implement user authentication using OAuth2.0 for secure access.
2. **Data Input**: Allow users to upload their cloud billing reports in CSV format. The application should support parsing these files to extract relevant information such as resource types, usage metrics, and costs.
3. **Provider Integration**: Use the 'ai-spend-ops' package to integrate with different cloud providers' APIs. Ensure that the application can fetch real-time cost and usage data from these providers.
4. **Cost Analysis**: Utilize 'ai-spend-ops' to analyze the uploaded data and real-time data from cloud providers. The analysis should include identifying over-provisioned resources, underutilized services, and potential cost-saving opportunities.
5. **Recommendations Engine**: Based on the analysis, generate personalized recommendations for each user. These could include suggestions like resizing instances, shutting down unused resources during off-hours, switching to cheaper instance types, etc.
6. **Dashboard**: Develop a user-friendly dashboard where users can view their current spend, cost trends over time, and the impact of implementing the recommended changes.
7. **Notification System**: Implement a notification system that alerts users when their spending exceeds a certain threshold or when significant cost-saving opportunities arise.
8. **API Documentation**: Provide comprehensive API documentation for developers who wish to integrate 'CostOptimizer' into their existing systems.

The 'ai-spend-ops' package is crucial for this project as it provides the necessary tools and APIs to handle multi-cloud cost management tasks efficiently. It simplifies the process of fetching data from various cloud providers and offers advanced algorithms for cost optimization. Your task is to design and implement this application from scratch, ensuring that it leverages the full capabilities of 'ai-spend-ops'.