animyst

v0.2.1 suspicious
5.0
Medium Risk

ANIMYST — Describe what you want, walk away, come back to a working repo. Autonomous build rites.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risk due to potential unnecessary shell executions and incomplete metadata.

  • Shell risk observed, possibly for git operations and tmux session management.
  • Author metadata is sparse and potentially unreliable.
Per-check LLM notes
  • Network: No network calls detected, indicating no direct external communication.
  • Shell: Shell executions observed are likely related to git operations and tmux session management, which may be part of the intended functionality but warrant further investigation into their necessity and context.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags, including an author with minimal information and a new or inactive account, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9129 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

  • 24 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 42 commits in CreatorGodMode/animystcli
  • Single author but highly active (42 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • e(slug: str) -> bool: r = subprocess.run( ["tmux", "has-session", "-t", tmux_session_name(slu
  • (path) try: out = subprocess.check_output( ["git", "-C", str(path), "log", "-1", "--format
  • t"] = "" try: n = subprocess.check_output( ["git", "-C", str(path), "rev-list", "--count",
  • = tmux_session_name(slug) subprocess.run(["tmux", "kill-session", "-t", session], capture_output=True
  • h} {rite_dir} {max_iter}" subprocess.run( ["tmux", "new-session", "-d", "-s", session, "-c",
  • rget.mkdir(parents=True) subprocess.run(["git", "init", "-q"], cwd=target, check=True) if not su
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: famished.ai>

✓ 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 animyst 
Create a mini-application called 'AnimeMystic' that leverages the 'animyst' package to automate the creation of a repository for an anime recommendation system. The application should allow users to input their favorite anime genres and titles, and then autonomously generate a personalized anime recommendation list based on their preferences. The process should be completely hands-off once the initial inputs are provided, allowing the user to come back later to a fully populated and functional recommendation system.

Key Features:
1. User Input: Allow users to enter their preferred anime genres and specific titles they love.
2. Autonomous Build Process: Use 'animyst' to automatically set up a GitHub repository with all necessary files and configurations for the recommendation system.
3. Recommendation Engine: Implement a basic recommendation engine that suggests new anime based on the user's input, utilizing external APIs like Jikan API for data retrieval.
4. Repository Population: Once the repository is set up, the application should populate it with the recommendation engine's output, including a README file summarizing the user's preferences and recommended anime list.
5. Notification System: After the repository is fully built and populated, notify the user via email or SMS that their personalized anime recommendation system is ready.

How 'animyst' is Utilized:
- Automate the creation of the GitHub repository using 'animyst', specifying the project name, description, and other relevant details.
- Configure 'animyst' to include necessary files and dependencies within the repository.
- Use 'animyst' to trigger the autonomous build process, which involves running scripts to fetch data, generate recommendations, and update the repository content.

💬 Discussion Feed

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