ai-producer

v0.3.5 suspicious
6.0
Medium Risk

Reusable agentic-orchestration library: SQLite-WAL ledger + Claude Agent SDK dispatcher with per-agent budgets and kill switch.

🤖 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.

💬 Discussion Feed

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