What is the primary purpose of this skill?

It provides a structured guide for developers to ensure any new LLM backend added to the Caro CLI properly validates generated shell commands against safety policies before execution.

Which inference backends are compatible with this skill?

It is designed for any inference backend integration, including local models like MLX, Ollama, and LM Studio, as well as remote APIs like Anthropic and OpenAI.

What kind of commands are blocked by the safety validator?

The validator identifies and blocks high-risk patterns such as recursive deletions (rm -rf), disk formatting (dd), and dangerous system-wide permission changes.

Does this skill work with languages other than Rust?

The implementation examples and integration patterns provided are specifically tailored for the Caro CLI's Rust-based architecture.

Backend Safety Integrator

Name: Backend Safety Integrator
Author: wildcard

bywildcard

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安全与测试

Standardizes safety validation and command auditing when integrating new LLM inference backends into the Caro Rust CLI.

关于

The Backend Safety Integrator skill provides a rigorous, 6-phase framework for developers to implement safety checks when adding or updating inference backends—such as MLX, Anthropic, or OpenAI—to the Caro CLI. It ensures that every command generated by an LLM is passed through a CommandValidator to block destructive operations like recursive deletions or disk wiping before user execution. By following this systematic workflow, developers can maintain the security integrity of natural-language-to-POSIX tools, preventing accidental or malicious system damage while providing clear, risk-aware error feedback.

主要功能

Automated blocking of destructive operations like 'rm -rf' and disk wiping
Rust-based safety validation patterns for LLM-generated commands
Standardized error handling for high-risk pattern reporting
Structured 6-phase workflow for secure backend integration
End-to-end testing protocols for validating backend security guardrails
23 GitHub stars

使用场景

Upgrading security protocols for existing cloud-based LLM integrations
Adding a new local inference backend like MLX or Ollama to the Caro CLI
Implementing guardrails to prevent natural language tasks from executing dangerous shell commands

关于

主要功能

Automated blocking of destructive operations like 'rm -rf' and disk wiping
Rust-based safety validation patterns for LLM-generated commands
Standardized error handling for high-risk pattern reporting
Structured 6-phase workflow for secure backend integration
End-to-end testing protocols for validating backend security guardrails
23 GitHub stars

使用场景

Upgrading security protocols for existing cloud-based LLM integrations
Adding a new local inference backend like MLX or Ollama to the Caro CLI
Implementing guardrails to prevent natural language tasks from executing dangerous shell commands