aircover-pipeline

v1.0.3 suspicious
4.0
Medium Risk

End-to-end Aircover pipeline: pull meetings + run a coaching agent on each.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential issues with metadata, but no direct evidence of malicious intent or activity was found in its codebase.

  • High metadata risk due to recent repository creation and low activity.
  • No direct signs of network, shell, obfuscation, or credential risks.
Per-check LLM notes
  • Network: The detected network calls seem to be related to authentication and token refreshing, which could be legitimate for API interactions.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to recent repository creation, low activity, and sparse maintainer history.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_aircover_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Aircover/aircover-pipeline#readme
  • Detailed PyPI description (11489 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

  • 23 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 5 commits in Aircover/aircover-pipeline
  • 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)

  • ) response = requests.post( f"{self._base_url}{LOGIN_PATH}", js
  • try: resp = requests.post( f"{self._base_url}{REFRESH_PATH}"
✓ 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: aircover.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 6 day(s) ago (2026-05-31T21:28:43Z)

  • Repository created very recently: 6 day(s) ago (2026-05-31T21:28:43Z)
  • Repository has zero stars and zero forks
  • All 5 commits happened within 24 hours
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aircover-pipeline 
Create a personal productivity assistant app named 'AirCoach' using the Python package 'aircover-pipeline'. This app will help users optimize their meeting schedules and improve their communication skills through real-time feedback. Here’s how it works:

1. **Meeting Scheduling**: Integrate with popular calendar services like Google Calendar and Microsoft Outlook to automatically pull upcoming meetings.
2. **Pre-Meeting Preparation**: Before each meeting, provide users with tips and reminders based on the agenda and participants of the meeting.
3. **Real-Time Coaching**: During the meeting, use the 'aircover-pipeline' package to run a coaching agent that listens to the conversation and provides instant feedback on communication style, clarity, and engagement levels.
4. **Post-Meeting Review**: After the meeting, summarize key points discussed and offer personalized suggestions for improvement based on the meeting’s performance metrics.
5. **Feedback Loop**: Allow users to rate the effectiveness of the feedback received and adjust future coaching sessions accordingly.

Suggested Features:
- Integration with multiple calendar services.
- Customizable pre-meeting reminders and tips.
- Real-time notifications and coaching messages.
- Detailed post-meeting summaries and analytics.
- User-friendly interface for setting preferences and viewing progress.

Utilization of 'aircover-pipeline':
- Use 'aircover-pipeline' to automate the process of pulling meeting details from calendars.
- Leverage the coaching agent within 'aircover-pipeline' to analyze and provide feedback during meetings.
- Implement a feature to save and review past coaching sessions for continuous improvement.