security-best-practices
OfficialPerform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/repo
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What we know about this skill
Publisher
openai
Matched to the vendor's own GitHub organisation.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned openai/skills at 49f948faa9.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 49f948faa9
- Scanned
- 13 files, 9,053 lines
About this skill
What it does
This skill analyzes code and configurations to identify potential security vulnerabilities based on established best practices. It provides tailored recommendations for improvement, considering both language-specific nuances (e.g., Python, JavaScript) and framework considerations. The skill is designed to proactively enhance the security posture of AI applications by highlighting areas needing attention.
When to use it
- Pre-deployment Security Audit: Before releasing a new AI application or model, request a security review to identify and address vulnerabilities early on.
- Code Review Integration: Incorporate this skill into your code review process to ensure adherence to security best practices during development.
- Repository Assessment: Evaluate the security of existing repositories containing AI-related code and configurations.
- Framework Security Checks: Specifically assess the configuration and usage patterns within a particular AI framework (e.g., TensorFlow, PyTorch).
Key capabilities
- Language-specific security best practice review
- Framework-specific security best practice review
- Vulnerability identification
- Security improvement recommendations
Example prompts
- "Perform a security review of this Python script for potential vulnerabilities."
- "Can you assess the security configuration of my TensorFlow model deployment?"
- "Review this repository and suggest improvements based on AI security best practices."
Tips & gotchas
The skill is triggered only when explicitly requested. Ensure your prompt clearly indicates a need for security guidance or review; otherwise, it will not engage these capabilities.