awen-weave

v0.1.1 suspicious
4.0
Medium Risk

Awen Weave — a pattern for weaving knowledge, place, data and human judgement into coherent living systems.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential network and shell execution vulnerabilities. While there are no clear signs of malicious intent, the sparse metadata and lack of community engagement raise concerns about its legitimacy.

  • moderate network risk
  • potential shell command execution issues
  • sparse maintainer information
Per-check LLM notes
  • Network: The network calls seem to be related to downloading files, which is common but should be reviewed for the legitimacy of URLs and handling of sensitive data.
  • Shell: Executing shell commands, especially those involving GitHub PR lists, could indicate integration with version control systems but also poses risks if not properly sanitized or authorized.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's information is sparse, and the repository lacks community engagement, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • 12 test file(s) detected (e.g. test_craidd_init.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://awenweave.com
  • Brief PyPI description (771 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

  • 220 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 63 commits in Huw-Lab/awen-weave
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • roduct_id}/downloads" r = requests.get(url, timeout=DEFAULT_TIMEOUT, headers={"User-Agent": USER_AG
  • suffix + ".partial") with requests.get( url, stream=True, timeout=DEFAULT_T
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • [] try: result = subprocess.run( ["gh", "pr", "list", "--state", "a
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: awenweave.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 awen-weave 
Create a mini-app called 'EcoWeaver' that utilizes the 'awen-weave' package to integrate environmental data with local community knowledge and expert judgment. The app should allow users to input real-time environmental data such as air quality, water quality, and temperature from their location. Additionally, it should incorporate insights and traditional ecological knowledge from local communities regarding these environmental factors. Users should also have the ability to consult expert opinions on interpreting and acting upon this combined data.

Step-by-Step Guide:
1. Set up the project environment by installing necessary packages including 'awen-weave'.
2. Design a user-friendly interface where users can log in and add their location-based environmental data.
3. Integrate 'awen-weave' to weave together the uploaded data with pre-existing databases of local ecological knowledge.
4. Implement a feature where users can ask questions about the integrated data and receive responses based on both the uploaded data and the woven knowledge.
5. Add functionality for experts to review and contribute their professional analysis on the data and knowledge provided.
6. Ensure the app provides recommendations based on the woven system for actions users can take to improve their local environment.
7. Test the app thoroughly to ensure all features work seamlessly and the 'awen-weave' integration functions correctly.
8. Deploy the app and promote its use within local communities and among environmental organizations.

Suggested Features:
- Real-time data visualization of environmental conditions.
- Interactive maps showing the geographical distribution of environmental issues.
- A discussion forum for users to share tips and success stories related to improving local environments.
- Notifications for significant changes in environmental data that may require immediate attention.
- A rating system for the reliability of different sources of information within the woven knowledge system.

💬 Discussion Feed

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