agent-readiness

v4.1.0 safe
3.0
Low Risk

Benchmark how agent-ready a code repository is for LLM coding agents.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no network calls, shell execution limited to git operations, and no signs of obfuscation or credential harvesting. The metadata suggests a new or less active author, but this alone does not warrant suspicion.

  • No network calls detected
  • Shell execution is limited to git operations
  • No obfuscation or credential harvesting patterns
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution is observed but appears to be related to git operations, suggesting it might be part of the package's intended functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, which may indicate a new or less active account, but there are no other red flags.

🔬 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)

  • d", "run_command") proc = subprocess.run( command, shell=True, cwd=str(repo),
  • ) try: proc = subprocess.run( command, shell=True, cw
  • nts too).""" result = subprocess.run( ["git", "rev-parse", "--git-dir"],
  • return 0 result = subprocess.run( ["git", "rev-list", "--count", "HEAD"],
  • ", "-print", ] proc = subprocess.run(cmd, capture_output=True, text=True, check=False) candid
  • return None proc = subprocess.run( ["git", "-C", str(p), "log", "-1", "--format=%cr"],
✓ 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

Repository harrydaihaolin/agent-readiness appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "agent-readiness contributors" 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 agent-readiness 
Create a web-based tool called 'CodePrep' that helps developers evaluate their code repositories for readiness to work with large language model (LLM) coding agents. The tool should provide a comprehensive analysis of the repository's structure, documentation quality, test coverage, and other relevant metrics to determine how well-prepared the codebase is for integration with LLMs. Here are the key steps and features you should implement:

1. **Repository Analysis**: Develop a feature that allows users to input a GitHub repository URL. Once submitted, the tool should use the 'agent-readiness' package to analyze the repository for agent-readiness.
2. **Detailed Report Generation**: After analyzing the repository, generate a detailed report highlighting strengths and areas for improvement. This report should include scores for different categories such as code structure, documentation completeness, test coverage, and more.
3. **Interactive Dashboard**: Implement an interactive dashboard where users can view the analysis results in real-time. Include visualizations like graphs and charts to make the data more accessible.
4. **Customizable Alerts**: Allow users to set up customizable alerts based on specific criteria from the analysis results. For example, users could receive notifications if their repository's test coverage drops below a certain threshold.
5. **Integration with Popular IDEs**: Provide plugins or extensions for popular Integrated Development Environments (IDEs) like Visual Studio Code or PyCharm, enabling developers to directly analyze their projects within these environments.
6. **Continuous Monitoring**: Offer a continuous monitoring service where the tool periodically checks the repository's status and sends updates to subscribed users.

Throughout the development process, utilize the 'agent-readiness' package to benchmark and assess the repository's readiness for LLM coding agents, ensuring that your tool provides accurate and actionable insights.