🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a notable network risk due to its interaction with ntfy.sh, raising concerns about potential data exfiltration or misuse. However, other risks such as shell execution, obfuscation, and credential harvesting are minimal.

High network risk due to interaction with ntfy.sh
Low risk in other areas like shell execution, obfuscation, and credential harvesting

Per-check LLM notes

Network: The package makes network calls to ntfy.sh which could be used for logging or sending notifications, potentially indicating telemetry or alerting functionality but also possibly data exfiltration.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive information being stolen.
Metadata: The maintainer has a new or inactive account and the package lacks PyPI classifiers, suggesting low effort in maintaining it.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
23 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (14702 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

121 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 9.0

Found 6 network call pattern(s)

try: req = urllib.request.Request( f"https://ntfy.sh/{ntfy_topic}",
}, ) urllib.request.urlopen(req, timeout=5) logger.info("ntfy.sh not
{} def fake_urlopen(req: urllib.request.Request, timeout: float = 0): for name, value in req
s, dashes).""" sent: list[urllib.request.Request] = [] def fake_urlopen(req: urllib.request.Requ
[] def fake_urlopen(req: urllib.request.Request, timeout: float = 0): for _, value in req.he
_POLL_WINDOW}" resp = httpx.get(url, timeout=_REQUEST_TIMEOUT) resp.raise_for_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

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 4.0

2 maintainer concern(s) found

Author "capt-pancakes" 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-producer

Create a task management system called 'TaskMaster' using the Python package 'ai-producer'. TaskMaster will serve as a personal assistant that helps users manage their daily tasks efficiently. It will utilize the 'ai-producer' package's capabilities, such as SQLite-WAL ledger for storing task data and Claude Agent SDK dispatcher for orchestrating task execution. Here are the steps and features to include in your project:

1. **User Authentication**: Allow users to create accounts and log in securely. This ensures that each user's task data is stored privately.
2. **Task Creation & Management**: Users can add new tasks with descriptions, due dates, and priority levels. Tasks can also be marked as completed or deleted.
3. **Task Scheduling**: Implement a scheduler that uses the 'ai-producer' package's dispatcher to trigger reminders for upcoming tasks based on their due dates.
4. **Budget Management**: Integrate 'ai-producer's budget management feature to limit the number of tasks a user can create within a certain period, promoting efficient task planning.
5. **Kill Switch Mechanism**: Use the 'ai-producer' package's kill switch to halt any ongoing task execution if necessary, providing flexibility in managing task workflows.
6. **SQLite-WAL Ledger Integration**: Store all task-related data using the SQLite-WAL ledger provided by 'ai-producer', ensuring reliable and efficient data storage.
7. **Interactive CLI Interface**: Develop an interactive command-line interface where users can easily interact with TaskMaster to perform various operations like adding, deleting, or marking tasks as completed.
8. **Task Analytics**: Offer basic analytics about the user's task completion rate, average time taken to complete tasks, and other useful metrics.

By following these guidelines, you'll build a comprehensive task management system that leverages the powerful features of the 'ai-producer' package to enhance productivity and organization.