atomadic-forge

v0.87.0 safe
4.0
Medium Risk

Atomadic Forge ? absorb, enforce, emerge. Polyglot (Python ? JavaScript/TypeScript ? Rust ? Go ? Swift ? Kotlin) architecture guardian for AI-generated code.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across most categories, with only minor concerns regarding network and shell execution risks. There's no evidence of malicious activity.

  • Network risk due to potential interaction with external services.
  • Shell risk due to possible execution of system commands.
Per-check LLM notes
  • Network: Network calls could be legitimate if the package is designed to interact with external services.
  • Shell: Shell execution suggests the package might perform operations like version control actions, which could be intended but also pose risks if not properly controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities were flagged.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (16632 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

  • 234 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 6.0

Found 4 network call pattern(s)

  • B_SEARCH}?{params}" req = urllib.request.Request(url, headers={"User-Agent": _USER_AGENT,
  • json"}) try: with urllib.request.urlopen(req, timeout=10) as resp: data = json.lo
  • .extra_headers) req = urllib.request.Request( endpoint, data=body, headers=headers, m
  • try: with urllib.request.urlopen(req, timeout=self.timeout_s) as resp:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: r = subprocess.run( list(args), cwd=str(project_root),
  • """ try: result = subprocess.run( ["git", "log", "--oneline", f"v{since_ref}..HEA
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Atomadic" 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 atomadic-forge 
Create a versatile code generator tool called 'CodeMorpher' using the Python package 'atomadic-forge'. This tool will allow users to input a basic code snippet or algorithm description in plain English and generate equivalent code in multiple programming languages (JavaScript/TypeScript, Rust, Go, Swift, Kotlin). The application should have a user-friendly interface where users can select their desired output language(s) from a dropdown menu. Additionally, it should provide options to customize the generated code by adding comments, choosing different coding styles (e.g., camelCase vs snake_case), and adjusting complexity levels of the generated code snippets.

The core functionality of CodeMorpher involves absorbing the user's input, enforcing structure and syntax according to the selected target language(s), and emerging with polished, functional code outputs. Utilize 'atomadic-forge' to ensure the generated code adheres to best practices and standards of each language while maintaining consistency across all generated versions.

Step-by-step guide:
1. Design a simple web interface where users can enter their code description or snippet.
2. Implement a backend service using Flask or Django to handle user inputs and process requests.
3. Integrate 'atomadic-forge' into the backend to manage the generation process and ensure quality control over the output code.
4. Develop algorithms or use pre-existing models within 'atomadic-forge' to translate the input into the selected languages.
5. Allow users to preview and download the generated code snippets.
6. Test the application thoroughly with various inputs and edge cases to ensure reliability and accuracy.
7. Deploy the application on a cloud platform like AWS or Heroku for easy access.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!