🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate based on its benign network behavior, lack of shell execution or obfuscation, and no evidence of credential harvesting. The slightly elevated metadata risk due to low activity and missing maintainer information does not conclusively indicate malicious intent.

Low network, shell, obfuscation, and credential risks
Elevated metadata risk due to limited maintainer information

Per-check LLM notes

Network: The use of session management and asynchronous clients with retry mechanisms is common for network operations in legitimate software, indicating likely benign network behavior.
Shell: No shell execution patterns were detected, suggesting there is no evidence of direct system command execution within the package.
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 potential low activity and lack of maintainer information, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (3.0/10)

○ 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 (14378 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

30 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 13 commits in svr-s/apiphany
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

ession self.session = requests.Session() retries = Retry(total=3, backoff_factor=1, status_
ne: self.client = httpx.AsyncClient(cert=self.cert, timeout=60.0) client_managed_her

✓ 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

Email domain looks legitimate: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 apiphany

Create a mini-application named 'APIAnalyzer' that leverages the Apiphany package to streamline the process of extracting and analyzing data from multiple APIs. This application will focus on fetching financial market data from various sources such as Alpha Vantage and Yahoo Finance, and then consolidating it into a single, structured dataset for further analysis. The goal is to provide users with a comprehensive view of stock performance across different exchanges and time periods.

### Features:
1. **Multi-API Support**: Define workflows to interact with both Alpha Vantage and Yahoo Finance APIs simultaneously.
2. **Authentication Handling**: Implement API key management for secure access to the services.
3. **Pagination & Chaining**: Fetch all available historical stock price data by chaining requests and handling pagination automatically.
4. **Rate Limiting**: Ensure efficient API usage by implementing rate limiting based on the providers' guidelines.
5. **Data Structuring**: Use Apiphany's capabilities to transform raw JSON responses into structured Pandas DataFrames.
6. **Data Analysis**: Provide basic statistical analysis on the fetched data, such as calculating moving averages, volatility, etc.
7. **Visualization**: Integrate Matplotlib or Seaborn to visualize the analyzed data, showing trends and comparisons between different stocks.
8. **State Tracking**: Keep track of the last fetched data point to avoid redundant requests and improve efficiency.
9. **Error Handling**: Implement robust error handling to manage issues like network errors, invalid responses, or exceeded quotas.

### Steps:
1. **Setup Environment**: Install necessary packages including Apiphany, Pandas, and visualization libraries.
2. **Configuration Model**: Define a configuration model using Apiphany to specify the APIs, endpoints, parameters, and transformations needed.
3. **Fetch Data**: Use Apiphany to execute the defined workflows concurrently, fetching data from both APIs.
4. **Process Responses**: Utilize Apiphany's JSON unpacking capabilities to convert the raw responses into structured DataFrames.
5. **Analysis & Visualization**: Perform statistical analysis on the structured data and generate visualizations to represent the findings.
6. **User Interface**: Develop a simple CLI or GUI interface where users can input stock symbols and desired time frames for analysis.
7. **Testing & Validation**: Test the application thoroughly to ensure accuracy and reliability of the data.
8. **Documentation**: Provide comprehensive documentation on how to use the application and customize it for other APIs or data sources.