AI Analysis
Final verdict: SUSPICIOUS
The package is suspected to be potentially suspicious due to missing author details and a non-existent repository, alongside legitimate network calls that require further scrutiny.
- Metadata risk with missing author details and non-existent repository
- Network risk due to external API calls requiring further investigation
Per-check LLM notes
- Network: The presence of network calls to an external URL could indicate legitimate functionality like API interactions, but requires further investigation to ensure it's not used for unauthorized data transmission.
- Shell: No shell execution patterns detected, indicating low risk of direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The package shows some red flags such as missing author details and a non-existent repository, but there are no clear signs of typosquatting or other malicious activities.
Package Quality Overall: Medium (5.2/10)
✦ High
Test Suite
9.0
Test suite present — 10 test file(s) found
10 test file(s) detected (e.g. test_cache.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/timurka/amochkaDetailed PyPI description (5719 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
163 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
{token_url}") resp = requests.post(token_url, json=payload, timeout=10) if resp.status_
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 amochka
Create a mini-application called 'AmoAnalytics' that leverages the 'amochka' Python package to extract, transform, and load (ETL) data from amoCRM into a PostgreSQL database. This application will serve as a tool for CRM users to analyze their sales pipeline data more effectively. Step 1: Set Up Your Environment - Install Python and PostgreSQL on your machine. - Use pip to install the 'amochka' package and any other necessary libraries such as psycopg2 for PostgreSQL interaction. Step 2: Design the Database Schema - Define the schema for storing leads, deals, contacts, and tasks from amoCRM in PostgreSQL. - Ensure that the schema allows for efficient querying and reporting. Step 3: Implement Data Extraction - Utilize 'amochka' to authenticate with amoCRM's API. - Write functions to fetch leads, deals, contacts, and tasks from amoCRM using 'amochka'. Step 4: Transform and Load Data - Create transformation functions to clean and format the data fetched from amoCRM. - Use 'amochka' to load the transformed data into the PostgreSQL database. - Schedule these operations to run periodically (e.g., daily) to keep the data up-to-date. Step 5: Build Reporting Features - Develop SQL queries to generate reports based on the data stored in PostgreSQL. - Integrate a simple web interface using Flask or Django to display these reports. - Include visualizations using a library like Matplotlib or Plotly to make the data more accessible. Suggested Features: - User authentication for accessing the web interface. - Real-time data refresh functionality. - Customizable report generation allowing users to filter and sort data. - Export options for reports (CSV, PDF). How 'amochka' is Used: - For authenticating and interacting with amoCRM's API to fetch data. - For handling the ETL process, ensuring data integrity and consistency during transfer. - For managing connections and interactions with PostgreSQL. Your goal is to create a functional, user-friendly tool that helps businesses gain insights from their amoCRM data.
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