An AI skill is a structured system prompt or set of custom instructions that gives AI assistants like ChatGPT, Claude, and Gemini specialized capabilities. It includes a clear role definition, specific knowledge areas, step-by-step processes, and quality criteria that help AI perform specific professional tasks.

How does Skill This work?

Simply describe your professional expertise in plain text. Our free AI skill generator analyzes your input and transforms it into a structured system prompt. The skill is then graded on clarity, specificity, and usefulness. You can share your skill via a unique URL or copy it to use as custom instructions with any AI assistant like ChatGPT, Claude, or Gemini.

Is Skill This free to use?

Yes, Skill This is completely free. No account or payment is required. You can create unlimited skills and share them with anyone.

How is my skill graded?

Skills are graded on a scale from A+ to F (0-100 points). The grade reflects how clear, specific, and actionable your skill is. Higher grades indicate skills that AI assistants can use more effectively.

How do I use my skill with an AI assistant?

Copy your generated skill and paste it into the system prompt or custom instructions of any AI assistant. For ChatGPT, use Personalization > Custom Instructions. For Claude, add it to a Project's instructions. For Cursor or other IDEs, save it as a .cursorrules or CLAUDE.md file. The AI will then adopt that skill and perform tasks according to your expertise.

Building AI Workstations - AI Skill Graded A- | Skill This

Quick Start15 / 15

Check system readiness:

Bash
# System info
uname -a && lsb_release -a
free -h && df -h
lscpu | grep -E "(Model name|Thread|Core)"
nvidia-smi || echo "No NVIDIA GPU detected"

# Essential tools check
which python3 git docker curl wget || echo "Missing tools detected"

Recommendation▾

Add actual input/output pairs for the system readiness check showing what good vs problematic output looks like

Workflow15 / 15

Phase 1: Base System Setup

Progress:

Verify Ubuntu/Debian system requirements
Install essential development tools
Configure GPU drivers (if NVIDIA)
Set up Python environment
Install and configure Docker

Phase 2: AI Infrastructure

Progress:

Install local LLM runtime (Ollama recommended)
Set up model storage on appropriate drive
Test GPU acceleration
Configure development environment

Phase 3: Agent Framework

Progress:

Set up coding agent tools
Configure browser automation
Implement safety boundaries
Test agent workflows

Recommendation▾

Include specific error messages and their solutions in the troubleshooting examples rather than just general categories

Base System Setup

Essential packages:

Bash
sudo apt update && sudo apt install -y \
  build-essential curl wget git vim \
  python3 python3-pip python3-venv \
  apt-transport-https ca-certificates \
  software-properties-common \
  htop tree jq unzip

NVIDIA GPU setup (if applicable):

Bash
# Check GPU
lspci | grep -i nvidia

# Install drivers
sudo apt install nvidia-driver-535 nvidia-cuda-toolkit
sudo reboot

# Verify after reboot
nvidia-smi

Docker setup:

Bash
# Install Docker
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list

sudo apt update && sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

# Configure user permissions
sudo usermod -aG docker $USER
newgrp docker

# Test
docker run hello-world

Local AI Setup

Ollama installation:

Bash
# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start service
sudo systemctl start ollama
sudo systemctl enable ollama

# Test with small model
ollama pull phi3:mini
ollama run phi3:mini "Hello, test response"

Model storage configuration:

Bash
# Check disk space
df -h

# If moving to larger drive
sudo systemctl stop ollama
sudo mkdir -p /mnt/models/ollama
sudo chown -R $USER:$USER /mnt/models
echo 'OLLAMA_MODELS=/mnt/models/ollama' | sudo tee -a /etc/environment

# Restart and verify
sudo systemctl start ollama
ollama list

Development Environment

Python setup:

Bash
# Create project structure
mkdir -p ~/ai-workspace/{models,agents,tools}
cd ~/ai-workspace

# Virtual environment
python3 -m venv venv
source venv/bin/activate

# Essential packages
pip install --upgrade pip
pip install fastapi uvicorn requests python-dotenv
pip install openai anthropic ollama-python
pip install selenium beautifulsoup4 playwright

VS Code with AI extensions:

Bash
# Install VS Code
wget -qO- https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > packages.microsoft.gpg
sudo install -o root -g root -m 644 packages.microsoft.gpg /etc/apt/trusted.gpg.d/
sudo sh -c 'echo "deb [arch=amd64,arm64,armhf signed-by=/etc/apt/trusted.gpg.d/packages.microsoft.gpg] https://packages.microsoft.com/repos/code stable main" > /etc/apt/sources.list.d/vscode.list'

sudo apt update && sudo apt install code

# Launch and install Python, Docker, Remote extensions
code ~/ai-workspace

Examples17 / 20

Example 1: Local API Server Input: Need API endpoint for local model Output:

Python
# api.py
from fastapi import FastAPI
import ollama

app = FastAPI()

@app.post("/chat")
async def chat(prompt: str):
    response = ollama.chat(model='phi3:mini', messages=[
        {'role': 'user', 'content': prompt}
    ])
    return {"response": response['message']['content']}

# Run: uvicorn api:app --host 0.0.0.0 --port 8000

Example 2: Safe Coding Agent Input: Automate code reviews in workspace Output:

Python
# coding_agent.py
import os
import subprocess
from pathlib import Path

class SafeCodeAgent:
    def __init__(self, workspace_path):
        self.workspace = Path(workspace_path).resolve()
        
    def review_file(self, filepath):
        if not self._is_safe_path(filepath):
            raise ValueError("File outside workspace")
        
        # Analysis logic here
        
    def _is_safe_path(self, filepath):
        return Path(filepath).resolve().is_relative_to(self.workspace)

Recommendation▾

Provide concrete performance benchmarks or success criteria for each setup phase (e.g., 'Ollama should respond in <2s for phi3:mini')

Best Practices

System Management:

Use systemd for service management
Keep models on fastest/largest drive
Monitor GPU memory with nvidia-smi -l 1
Regular cleanup: docker system prune -a

Security Boundaries:

Run agents in workspace-scoped containers
Use read-only mounts for sensitive data
Implement file path validation
Never give agents sudo access

Performance Optimization:

GPU: Use CUDA-enabled containers
RAM: Monitor with htop, consider swap on SSD
Storage: SSD for models, HDD for archives
Network: Test local vs cloud latency

Common Pitfalls

GPU Issues:

NVIDIA driver mismatch → sudo apt purge nvidia-* && reinstall
CUDA version conflicts → Use Docker with specific CUDA base
GPU memory exhaustion → Monitor with nvidia-smi

Docker Problems:

Permission denied → Check user in docker group: groups $USER
Port conflicts → docker ps and netstat -tlnp
Storage full → docker system df && docker system prune

Python Environment:

Multiple Python versions → Use explicit python3
Package conflicts → Fresh venv for each project
Missing system deps → Install dev packages with apt

Agent Safety:

Unbounded file access → Implement path validation
Resource exhaustion → Set container limits
Network access → Use restricted Docker networks
Persistent state → Scope to project directories

Path and Environment Issues:

Stale symlinks → find -L . -type l
Wrong PATH → Check with echo $PATH
Environment not activated → which python
Service conflicts → systemctl status service-name