AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity with very low risks across all categories assessed. It is considered safe for use.
- No network calls detected
- No shell execution patterns
- No obfuscation patterns
- No credential harvesting patterns
Per-check LLM notes
- Network: No network calls detected, which is normal for a library focused on AWS CDK constructs and not performing direct cloud interactions.
- Shell: No shell execution patterns detected, which is expected as the package does not involve system-level operations.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious flags were raised.
Package Quality Overall: Medium (5.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4083 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
Classifier: Typing :: Typed
✦ High
Multiple Contributors
10.0
Active multi-contributor project
32 unique contributor(s) across 100 commits in aws/aws-cdkActive community — 5 or more distinct 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 aws/aws-cdk appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Amazon Web Services" 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 aws-cdk.aws-kinesisanalytics-flink-alpha
Create a real-time data processing application using AWS CDK and the 'aws-cdk.aws-kinesisanalytics-flink-alpha' package. This application will demonstrate the power of streaming analytics by processing live stock market tickers. The app will consist of the following components: 1. **Data Source**: Set up a Kinesis Data Stream to ingest real-time stock ticker data. 2. **Processing Engine**: Use Kinesis Data Analytics Flink to process the incoming data streams in real-time, calculating rolling averages and detecting anomalies in stock prices. 3. **Output**: Store the processed data into a Kinesis Data Firehose stream, which then loads the data into an S3 bucket for further analysis. 4. **Visualization**: Integrate with a simple web dashboard (using Flask) to display real-time stock price trends and anomalies detected. ### Features: - Real-time ingestion and processing of stock ticker data. - Calculation of rolling averages over a specified time window. - Detection of stock price anomalies based on deviations from the rolling average. - Storage of processed data in S3 for historical analysis. - Web-based visualization of stock price trends and anomalies. ### Utilization of 'aws-cdk.aws-kinesisanalytics-flink-alpha': - Configure the Kinesis Data Stream as the input source for your Flink application. - Define a Flink SQL job within the CDK construct to calculate rolling averages and detect anomalies. - Output the results of the Flink job to a Kinesis Data Firehose delivery stream for archival purposes. Your task is to design and implement this application using the AWS CDK, focusing on the integration of 'aws-cdk.aws-kinesisanalytics-flink-alpha'. Provide a clear, modular structure for your codebase and ensure that all necessary resources are properly configured and deployed using AWS CDK constructs. Additionally, include instructions for setting up and running the Flask-based web dashboard locally to visualize the processed data.
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