agentcode-cli

v1.2.0 suspicious
4.0
Medium Risk

An open, multi-model agentic coding CLI — inspired by Claude Code

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some potential risks, primarily due to shell execution capabilities and a lack of community engagement, which may suggest a lower level of scrutiny and maturity.

  • Shell execution present but without clear context or parameters.
  • Repository maintainer appears new and lacks community engagement.
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution is present but without clear context or parameters that indicate malicious intent, suggesting potential for user interaction or automation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer seems new and the repository lacks community engagement.

🔬 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)

  • ] = str(val) try: subprocess.run(cmd, shell=True, env=env, timeout=10, capture_output=True)
  • """ try: result = subprocess.run( command, shell=True, ca
  • ppend(path) result = subprocess.run( cmd, capture_output=True, text=True, timeout=15
  • .CompletedProcess: return subprocess.run(args, capture_output=True, text=True, timeout=timeout, cwd=o
  • : subprocess.run(cmd, shell=True, env=env, timeout=10, capture_output=True) except Excep
  • command, shell=True, capture_output=True, text=True,
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Vignesh Pai" 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 agentcode-cli 
Your task is to develop a fully-functional mini-application named 'CodeCraft' using the Python package 'agentcode-cli'. This application aims to streamline the process of generating, editing, and managing code snippets across various programming languages, leveraging the power of AI agents inspired by Claude Code.

Step 1: Define the Application's Purpose
- CodeCraft will serve as an interactive coding assistant that allows users to generate new code snippets based on their requirements, edit existing code snippets, and manage them efficiently.

Step 2: Core Features
- **Code Generation**: Users should be able to provide a brief description or a problem statement, and the application should generate relevant code snippets in multiple programming languages (e.g., Python, JavaScript).
- **Code Editing**: After generating code, users should have the ability to request modifications to the code snippet, such as adding comments, refactoring, or optimizing the code.
- **Snippet Management**: Implement a feature where users can save their code snippets locally, categorize them into different folders, and search through their saved snippets.
- **Interactive Mode**: Offer an interactive mode where users can converse with the AI agent about their code, asking questions, getting explanations, or seeking advice on best practices.

Step 3: Utilizing 'agentcode-cli'
- Use 'agentcode-cli' to handle the interaction with AI agents for code generation and editing. Integrate the package to enable seamless communication between the user interface and the AI backend.
- For each feature, demonstrate how you utilize specific functionalities from 'agentcode-cli' to achieve the desired outcome. For example, use 'agentcode-cli' commands to invoke AI agents for code generation and editing requests.

Step 4: Implementation Details
- Design a simple yet intuitive user interface for interacting with CodeCraft. Consider using command-line interfaces (CLI) or a graphical user interface (GUI) based on your preference and expertise.
- Ensure that all interactions with 'agentcode-cli' are well-documented within the application's codebase, explaining how and why each function call is made.
- Include error handling mechanisms to manage scenarios where the AI agents might not understand the user's request or if there are issues with the 'agentcode-cli' package itself.

Step 5: Testing and Documentation
- Write comprehensive test cases to validate the functionality of each feature within CodeCraft.
- Document the entire development process, including setup instructions, usage guidelines, and troubleshooting tips, making it accessible for other developers to contribute or use the application.

💬 Discussion Feed

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