apache-airflow-providers-atlassian-jira

v3.3.3 safe
3.0
Low Risk

Provider package apache-airflow-providers-atlassian-jira for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no signs of malicious activity. It has minor issues that do not significantly impact its safety.

  • Low risk scores across all categories
  • No evidence of shell execution, obfuscation, or credential harvesting
Per-check LLM notes
  • Network: The use of proxies in network calls is common for managing connections and handling requests, especially when interacting with external services like Atlassian JIRA.
  • Shell: No shell execution patterns were detected, which is normal and expected.
  • Obfuscation: The observed pattern is likely a standard practice for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has some minor issues but does not appear to be malicious or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-atl
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3719 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 11 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • f.proxies async with aiohttp.ClientSession(**session_kwargs) as session: return await super
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ 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: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ 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 apache-airflow-providers-atlassian-jira 
Your task is to create a mini-application using Apache Airflow that integrates with Atlassian Jira to automate the management of Jira issues based on certain conditions. This application will help streamline the process of issue tracking and management, making it more efficient and less prone to human error.

### Application Overview:
- **Name:** Jira Issue Automation
- **Purpose:** To automatically update the status of Jira issues based on predefined rules.

### Core Features:
1. **Integration with Jira:** Use the `apache-airflow-providers-atlassian-jira` package to connect your Apache Airflow DAGs with Jira.
2. **Issue Status Updates:** Automatically change the status of Jira issues based on specific criteria, such as due dates, assignee availability, or custom fields.
3. **Notifications:** Send notifications via email or Slack when an issue status changes or if an action needs to be taken.
4. **Custom Rule Engine:** Allow users to define their own rules for updating issue statuses.
5. **Logging and Monitoring:** Keep a log of all actions performed by the application and provide monitoring capabilities through Airflow's UI.

### Detailed Steps:
1. **Setup Environment:** Ensure you have Apache Airflow installed and configured. Install the `apache-airflow-providers-atlassian-jira` package using pip.
2. **Create Jira Connection:** In Airflow's UI, create a connection to your Jira instance using the `jira` connection type provided by the `apache-airflow-providers-atlassian-jira` package.
3. **Develop DAGs:** Write DAGs that use the JiraHook from the `apache-airflow-providers-atlassian-jira` package to interact with Jira APIs.
   - Example: Create a DAG that checks for overdue tasks daily and updates their status to 'Overdue'.
4. **Implement Custom Rules:** Develop a system where users can input custom rules for changing issue statuses. These rules could be based on conditions like due date, assignee availability, etc.
5. **Add Notification Mechanism:** Implement a notification system that sends alerts whenever an issue status changes. This could be done using Slack or Email operators available in Airflow.
6. **Logging and Monitoring:** Ensure that all actions performed by the application are logged properly. Use Airflow's built-in monitoring tools to keep track of the DAG's execution.
7. **Testing:** Thoroughly test the application with various scenarios to ensure reliability and accuracy.
8. **Documentation:** Provide clear documentation on how to set up and use the application, including examples of custom rules and configurations.

### Expected Outcome:
By the end of this project, you will have a functional mini-application that can automatically manage Jira issues based on predefined rules, significantly improving the efficiency of issue tracking and management processes.

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