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ai-engineer

Orchestrate LLM/RAG workflows with WSL2-gated execution and PII sanitization.

1|Updated Mar 30, 2026
Search Tags:#automation#security#ai#rag#orchestration#wsl2
One-click install
npx skills add https://github.com/FacundoSu1986/Sky-Claw --skill ai-engineer-facundosu1986
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-engineer
Source: https://github.com/FacundoSu1986/Sky-Claw/tree/main/.agents/skills/ai-engineer
Command: npx skills add https://github.com/FacundoSu1986/Sky-Claw --skill ai-engineer-facundosu1986

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, tomli, and includes scripts (resource) components.

What problem does it solve?

AI engineering work often involves coordinating Python/Node.js logic, local vector databases (Qdrant/SQLite-VSS), and multi-LLM orchestration, all while keeping code execution secure and isolated.

Core Features & Use Cases

  • Safe sandboxing via a WSL2 gateway and PII sanitization to protect data during model calls.
  • Template-driven orchestration with an agent_config.yaml and a deployable RAG pipeline scaffold for rapid production testing.
  • Local-first architecture that supports Python/Node.js AI logic, vector stores, and HITL workflows for controlled experimentation.

Quick Start

Run the WSL2 gateway to safely sandbox and test your Python and YAML AI configurations.

Frequently Asked Questions about ai-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I securely orchestrate AI agent workflows with Python and YAML configurations?▼

You can securely orchestrate AI agent workflows by running Python and YAML configurations through a WSL2 gateway script, which enforces safe sandboxing, gated execution, and PII sanitization during model calls.

What is the best way to scaffold a local RAG pipeline for testing?▼

The best way to scaffold a local RAG pipeline is using a template-driven orchestration approach with an agent_config.yaml, enabling rapid production testing alongside local vector databases like Qdrant or SQLite-VSS.

Does this AI orchestration approach support human-in-the-loop (HITL) workflows?▼

Yes, this AI orchestration approach supports human-in-the-loop workflows by providing a defined deployment template that includes robust logging and a HITL policy for controlled experimentation.

Can I use WSL2 to sandbox and test Node.js AI logic safely?▼

Yes, you can use the WSL2 gateway to sandbox and test Node.js AI logic safely, as the local-first architecture supports both Python and Node.js for multi-LLM orchestration.

How do I enforce PII sanitization during multi-LLM orchestration?▼

You enforce PII sanitization during multi-LLM orchestration by executing your AI engineering workflows through the WSL2 gateway script, which protects sensitive data during local model calls.

What are the limitations of using WSL2 for local vector database orchestration?▼

The WSL2 gateway isolates execution and enforces gated policies for local vector databases, but requires a Windows environment with WSL2 configured to properly run the Python and Node.js agent logic.

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