🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and unusual commit patterns, raising concerns about its legitimacy and intent.

Unconventional and potentially obfuscated code
Recent burst of commits with low-effort metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: Subprocess execution might be legitimate for some operations, but requires scrutiny to ensure it's not being used for malicious purposes.
Obfuscation: The code uses unconventional and possibly unnecessary methods for time conversion, suggesting potential obfuscation to hide functionality.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The recent burst of commits and low-effort metadata suggest potential risk, but insufficient evidence for high confidence.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

, 315532800) return tuple(__import__("time").localtime(timestamp)[:6]) def _should_skip(path: Path) ->

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

es with uv") try: subprocess.run(command, check=True) except subprocess.CalledProcessErro

✓ 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: All 8 commits happened within 24 hours

All 8 commits happened within 24 hours

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "minorun365" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentcore-push

Your task is to develop a streamlined application named 'AgentDeployer' using Python, which leverages the 'agentcore-push' package to facilitate the deployment of Python Strands Agents to Amazon Bedrock AgentCore Runtime. This tool aims to simplify the process of uploading agents, allowing developers to focus on crafting sophisticated AI behaviors without worrying about the underlying infrastructure setup.

**Project Scope:**
- Create a command-line interface (CLI) that accepts a path to a Python Strands Agent file as input.
- Integrate the 'agentcore-push' package to handle the upload process.
- Implement error handling to manage common issues such as network failures, incorrect file paths, and unsupported file types.
- Provide feedback to the user during the upload process, including progress indicators and success/failure notifications.
- Ensure the application supports multiple agents being uploaded in a single session.

**Suggested Features:**
1. **Batch Upload Capability:** Allow users to specify a directory containing multiple agent files for batch upload.
2. **Configuration File Support:** Enable users to define agent configurations in a YAML file, specifying details like agent name, version, and runtime settings.
3. **Logging Mechanism:** Implement a logging system to record all actions taken by the application, including timestamps, actions performed, and any errors encountered.
4. **Interactive Mode:** Offer an interactive mode where users can input agent file paths directly via the CLI.
5. **Progressive Uploads:** Display a real-time progress bar showing the status of each agent upload.
6. **Custom Error Messages:** Provide clear and informative error messages when uploads fail.
7. **Environment Variable Support:** Allow configuration options to be set via environment variables for easier integration into automated workflows.
8. **Help Documentation:** Include comprehensive help documentation within the CLI, accessible via command line flags.

**Utilizing 'agentcore-push':**
- Use the 'agentcore-push' package's primary function to initiate the upload process. Ensure you understand its API thoroughly, including any required parameters and return values.
- Handle exceptions raised by 'agentcore-push' gracefully, providing meaningful feedback to the user.
- Explore additional functionalities provided by 'agentcore-push', such as support for different agent formats or runtime configurations, to enhance your application's capabilities.

By following these guidelines, you will create a robust and user-friendly tool that significantly simplifies the deployment of Python Strands Agents to Amazon Bedrock AgentCore Runtime.