Li_python_sec_check

What This Skill Does

Li_python_sec_check is a specialized OpenClaw agent skill designed to audit Python source code for security vulnerabilities and adherence to industry-standard best practices. By combining the rigorous requirements of CloudBase infrastructure guidelines with comprehensive Tencent security recommendations, this tool provides a multi-layered analysis of your codebase. The skill operates primarily through a local execution engine to ensure data privacy, with an optional LLM-assisted analysis mode that can be enabled for deep semantic vulnerability detection when complex logic assessment is required.

Installation

To integrate this security tool into your OpenClaw environment, execute the following command in your terminal or ClawHub interface: clawhub install openclaw/skills/skills/43622283/li-python-sec-check Once installed, the skill will be available as an active agent module, ready to scan files or directory paths passed to the assistant.

Use Cases

This skill is indispensable for developers and DevOps engineers aiming to harden their Python applications. Common use cases include:

• Conducting automated pre-commit security audits to detect common pitfalls like hardcoded secrets, SQL injection vulnerabilities, or insecure cryptographic implementations.
• Ensuring compliance with enterprise-grade security standards during the code review process.
• Automating the detection of outdated or vulnerable third-party library dependencies.
• Providing pedagogical feedback to junior developers on secure coding patterns in Python.

Example Prompts

1. "Analyze the current project directory using li_python_sec_check and report any high-priority security vulnerabilities."
2. "Run a security audit on the file 'database_connector.py' and suggest refactoring tips based on CloudBase security guidelines."
3. "Scan my entire codebase for hardcoded API keys or sensitive credentials and explain how to mitigate each finding."

Tips & Limitations

• Local First: The tool defaults to local static analysis. For the most sensitive code, keep the LLM-assisted mode disabled to prevent source code from being sent to external models.
• Scope: While the tool excels at pattern matching and vulnerability identification, it should not replace human security audits for critical production systems.
• Context: Always provide the full scope of your project directory when possible, as the tool performs better when it can analyze cross-file dependencies and imports. The skill currently focuses primarily on Python; it will not offer relevant analysis for other languages within your repository.