Instructions to use kurtpayne/skillscan-detector-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kurtpayne/skillscan-detector-v4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kurtpayne/skillscan-detector-v4",
	filename="skillscan-detector-v4-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use kurtpayne/skillscan-detector-v4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Use Docker

docker model run hf.co/kurtpayne/skillscan-detector-v4:Q4_K_M

LM Studio
Jan

vLLM

How to use kurtpayne/skillscan-detector-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kurtpayne/skillscan-detector-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kurtpayne/skillscan-detector-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kurtpayne/skillscan-detector-v4:Q4_K_M

Ollama
How to use kurtpayne/skillscan-detector-v4 with Ollama:
```
ollama run hf.co/kurtpayne/skillscan-detector-v4:Q4_K_M
```

Unsloth Studio new

How to use kurtpayne/skillscan-detector-v4 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kurtpayne/skillscan-detector-v4 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kurtpayne/skillscan-detector-v4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kurtpayne/skillscan-detector-v4 to start chatting

Pi new

How to use kurtpayne/skillscan-detector-v4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kurtpayne/skillscan-detector-v4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kurtpayne/skillscan-detector-v4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kurtpayne/skillscan-detector-v4:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kurtpayne/skillscan-detector-v4:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use kurtpayne/skillscan-detector-v4 with Docker Model Runner:
```
docker model run hf.co/kurtpayne/skillscan-detector-v4:Q4_K_M
```

Lemonade

How to use kurtpayne/skillscan-detector-v4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kurtpayne/skillscan-detector-v4:Q4_K_M

Run and chat with the model

lemonade run user.skillscan-detector-v4-Q4_K_M

List all available models

lemonade list

SkillScan Detector v4

A fine-tuned Qwen2.5-1.5B-Instruct model for detecting security threats in AI agent skill files.

What it does

Analyzes AI agent skill files (.md) and outputs structured JSON with:

Verdict: benign or malicious
Labels: specific attack types detected
Confidence: 0-1 score
Reasoning: human-readable explanation citing evidence from the text

Attack types detected

Class	F1	Precision	Recall
path_traversal	0.857	0.882	0.833
social_engineering	0.857	0.750	1.000
prompt_injection	0.474	0.941	0.317
code_injection	0.424	0.438	0.412
supply_chain	0.340	0.258	0.500
evasion	0.308	0.182	1.000
data_exfiltration	0.148	0.080	1.000

Macro F1: 0.487 | Verdict accuracy: 85.2% | Parse failures: 1.2%

Usage with llama-cpp-python

Requirement already satisfied: llama-cpp-python in /opt/homebrew/lib/python3.11/site-packages (0.3.20) Requirement already satisfied: typing-extensions>=4.5.0 in /opt/homebrew/lib/python3.11/site-packages (from llama-cpp-python) (4.15.0) Requirement already satisfied: numpy>=1.20.0 in /opt/homebrew/lib/python3.11/site-packages (from llama-cpp-python) (1.26.4) Requirement already satisfied: diskcache>=5.6.1 in /opt/homebrew/lib/python3.11/site-packages (from llama-cpp-python) (5.6.3) Requirement already satisfied: jinja2>=2.11.3 in /opt/homebrew/lib/python3.11/site-packages (from llama-cpp-python) (3.1.4) Requirement already satisfied: MarkupSafe>=2.0 in /opt/homebrew/lib/python3.11/site-packages (from jinja2>=2.11.3->llama-cpp-python) (2.0.1)

Download the GGUF model: [33m⚠️ Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.[0m

Model details

Base model: Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0)
Fine-tuning: QLoRA (r=32, alpha=64) on 20,035 teacher-distilled examples
Teachers: Claude Sonnet + GPT-4o (structured security analysis)
Quantization: GGUF Q4_K_M (935 MB)
Inference: CPU-only via llama.cpp, ~2-4s per file
License: Apache 2.0

Files

File	Size	Description
	935 MB	Quantized model for CPU inference
	3.1 GB	Full FP16 weights

Part of SkillScan

This model is used by skillscan-security, the open-source CLI scanner for AI agent skill files.

Requirement already satisfied: skillscan-security[ml] in /opt/homebrew/lib/python3.11/site-packages (0.8.0) Requirement already satisfied: typer>=0.12.3 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (0.15.1) Requirement already satisfied: rich>=13.7.1 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (14.3.3) Requirement already satisfied: pydantic>=2.7.1 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (2.9.2) Requirement already satisfied: pyyaml>=6.0.1 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (6.0.1) Requirement already satisfied: nltk>=3.9.1 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (3.9.1) Requirement already satisfied: transformers>=4.40.0 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (5.5.1) Requirement already satisfied: torch>=2.2.0 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (2.6.0) Requirement already satisfied: peft>=0.9.0 in /opt/homebrew/lib/python3.11/site-packages (from skillscan-security[ml]) (0.14.0) Requirement already satisfied: click in /opt/homebrew/lib/python3.11/site-packages (from nltk>=3.9.1->skillscan-security[ml]) (8.1.7) Requirement already satisfied: joblib in /opt/homebrew/lib/python3.11/site-packages (from nltk>=3.9.1->skillscan-security[ml]) (1.4.2) Requirement already satisfied: regex>=2021.8.3 in /opt/homebrew/lib/python3.11/site-packages (from nltk>=3.9.1->skillscan-security[ml]) (2026.4.4) Requirement already satisfied: tqdm in /opt/homebrew/lib/python3.11/site-packages (from nltk>=3.9.1->skillscan-security[ml]) (4.67.0) Requirement already satisfied: numpy>=1.17 in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (1.26.4) Requirement already satisfied: packaging>=20.0 in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (24.2) Requirement already satisfied: psutil in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (6.1.0) Requirement already satisfied: accelerate>=0.21.0 in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (1.1.1) Requirement already satisfied: safetensors in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (0.4.5) Requirement already satisfied: huggingface-hub>=0.25.0 in /opt/homebrew/lib/python3.11/site-packages (from peft>=0.9.0->skillscan-security[ml]) (1.10.1) Requirement already satisfied: annotated-types>=0.6.0 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2.7.1->skillscan-security[ml]) (0.7.0) Requirement already satisfied: pydantic-core==2.23.4 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2.7.1->skillscan-security[ml]) (2.23.4) Requirement already satisfied: typing-extensions>=4.6.1 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2.7.1->skillscan-security[ml]) (4.15.0) Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/homebrew/lib/python3.11/site-packages (from rich>=13.7.1->skillscan-security[ml]) (3.0.0) Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/homebrew/lib/python3.11/site-packages (from rich>=13.7.1->skillscan-security[ml]) (2.18.0) Requirement already satisfied: filelock in /opt/homebrew/lib/python3.11/site-packages (from torch>=2.2.0->skillscan-security[ml]) (3.25.2) Requirement already satisfied: networkx in /opt/homebrew/lib/python3.11/site-packages (from torch>=2.2.0->skillscan-security[ml]) (3.4.2) Requirement already satisfied: jinja2 in /opt/homebrew/lib/python3.11/site-packages (from torch>=2.2.0->skillscan-security[ml]) (3.1.4) Requirement already satisfied: fsspec in /opt/homebrew/lib/python3.11/site-packages (from torch>=2.2.0->skillscan-security[ml]) (2024.9.0) Requirement already satisfied: sympy==1.13.1 in /opt/homebrew/lib/python3.11/site-packages (from torch>=2.2.0->skillscan-security[ml]) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/homebrew/lib/python3.11/site-packages (from sympy==1.13.1->torch>=2.2.0->skillscan-security[ml]) (1.3.0) Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /opt/homebrew/lib/python3.11/site-packages (from transformers>=4.40.0->skillscan-security[ml]) (0.22.2) Requirement already satisfied: shellingham>=1.3.0 in /opt/homebrew/lib/python3.11/site-packages (from typer>=0.12.3->skillscan-security[ml]) (1.5.4) Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /opt/homebrew/lib/python3.11/site-packages (from huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (1.4.3) Requirement already satisfied: httpx<1,>=0.23.0 in /opt/homebrew/lib/python3.11/site-packages (from huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (0.27.2) Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich>=13.7.1->skillscan-security[ml]) (0.1.2) Requirement already satisfied: MarkupSafe>=2.0 in /opt/homebrew/lib/python3.11/site-packages (from jinja2->torch>=2.2.0->skillscan-security[ml]) (2.0.1) Requirement already satisfied: anyio in /opt/homebrew/lib/python3.11/site-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (4.6.2.post1) Requirement already satisfied: certifi in /opt/homebrew/lib/python3.11/site-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (2024.8.30) Requirement already satisfied: httpcore==1.* in /opt/homebrew/lib/python3.11/site-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (1.0.7) Requirement already satisfied: idna in /opt/homebrew/lib/python3.11/site-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (3.10) Requirement already satisfied: sniffio in /opt/homebrew/lib/python3.11/site-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (1.3.1) Requirement already satisfied: h11<0.15,>=0.13 in /opt/homebrew/lib/python3.11/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub>=0.25.0->peft>=0.9.0->skillscan-security[ml]) (0.14.0) intel refresh updated=9 skipped=0 errors=0 Scan failed: Target does not exist: path/to/skill

Downloads last month: 1,012

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for kurtpayne/skillscan-detector-v4

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(181)

this model