Instructions to use yannelli/cli-command-risk-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use yannelli/cli-command-risk-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf yannelli/cli-command-risk-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yannelli/cli-command-risk-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf yannelli/cli-command-risk-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yannelli/cli-command-risk-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf yannelli/cli-command-risk-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf yannelli/cli-command-risk-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yannelli/cli-command-risk-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use yannelli/cli-command-risk-GGUF with Ollama:
```
ollama run hf.co/yannelli/cli-command-risk-GGUF:Q4_K_M
```
Unsloth Desktop

How to use yannelli/cli-command-risk-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yannelli/cli-command-risk-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "yannelli/cli-command-risk-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use yannelli/cli-command-risk-GGUF with Docker Model Runner:
```
docker model run hf.co/yannelli/cli-command-risk-GGUF:Q4_K_M
```

Lemonade

How to use yannelli/cli-command-risk-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yannelli/cli-command-risk-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.cli-command-risk-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use yannelli/cli-command-risk-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yannelli/cli-command-risk-GGUF: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 yannelli/cli-command-risk-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use yannelli/cli-command-risk-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yannelli/cli-command-risk-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "yannelli/cli-command-risk-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

cli-command-risk-GGUF / README.md

yannelli

Link the newer v1.5 release

1f127fb verified 14 days ago

preview code

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---
license: other
license_name: lfm1.0
license_link: LICENSE
base_model: yannelli/d1-3b-cli-command-risk
base_model_relation: quantized
datasets:
- yannelli/cli-command-risk
language:
- en
library_name: gguf
pipeline_tag: text-classification
tags:
- gguf
- llama.cpp
- ollama
- decision-model
- typed-decisions
- system-one
- shell
- guardrail
- command-risk
---
> A newer version is available: [CLI Command Risk v1.5 GGUF](https://huggingface.co/yannelli/cli-command-risk-v1.5-GGUF), trained on the dataset revision tagged [`v1.5`](https://huggingface.co/datasets/yannelli/cli-command-risk/tree/v1.5). These builds come from v1, which was trained on the revision tagged [`v1`](https://huggingface.co/datasets/yannelli/cli-command-risk/tree/v1).

![CLI Command Risk GGUF: GGUF builds of d1-3B CLI Command Risk for llama.cpp, plus Q8_0 and Q4_K_M files for Ollama.](https://huggingface.co/yannelli/cli-command-risk-GGUF/resolve/main/assets/cli-command-risk-gguf-banner.png)

# CLI Command Risk GGUF

GGUF builds of a decision model that rates the risk of a shell command, for llama.cpp and Ollama. Give the model a command and its shell, and it answers 17 typed questions with probabilities: the risk level from `safe` to `critical`, whether the command is risky, which of 13 risk categories apply, whether the effect can be undone without backups, and whether the harm needs root. The model scores answer options directly and generates no text.

The source model is [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B) fine-tuned with LoRA on [CLI Command Risk](https://huggingface.co/datasets/yannelli/cli-command-risk). Test-split results of the source model (PyTorch bf16, 12,689 commands, 215,713 decisions):

| Source model | Params | Macro F1 @ 0.5 | Level macro F1 | Accuracy | ECE | Builds here |
|---|---:|---:|---:|---:|---:|---|
| [d1-3B CLI Command Risk](https://huggingface.co/yannelli/d1-3b-cli-command-risk) | 3.1B | 0.929 | 0.669 | 0.963 | 0.030 | BF16, Q8_0, Q4_K_M |
Macro F1 averages the 13 risk-category questions at a 0.5 cutoff. Level macro F1 averages F1 over the 5 risk levels, using the most likely level. Accuracy counts every decision. The source model card holds the full results, training details and limitations.
## Files
| File | Size | Runtime |
|---|---:|---|
| `d1-3b-cli-command-risk-Q8_0.gguf` | 2.9 GB | llama.cpp `llama-server`, `/v1/systemone` |
| `d1-3b-cli-command-risk-Q4_K_M.gguf` | 1.7 GB | llama.cpp `llama-server`, `/v1/systemone` |
| `d1-3b-cli-command-risk-BF16.gguf` | 5.4 GB | llama.cpp `llama-server`, `/v1/systemone` |
| `ollama/d1-3b-cli-command-risk-Q4_K_M.gguf` + `ollama/Modelfile` | 1.7 GB | Ollama, with `ollama/ollama_d1.py` |
| `ollama/d1-3b-cli-command-risk-Q8_0.gguf` + `ollama/Modelfile.Q8_0` | 2.9 GB | Ollama, with `ollama/ollama_d1.py` |
| `questions.json` | 6 KB | The 17 questions the model was trained on |

Use Q8_0. It matches the PyTorch model on 99.9% of top answers in the CPU benchmark below. Q4_K_M changes 2.1% of top answers, so use it only when memory is tight.
## Questions
`questions.json` holds the 17 questions in the System One schema:
- `risk_level`: a score from 0 (`safe`) to 4 (`critical`). Each level's definition is in the question's criteria.
- `risky`: a yes/no question (`noul`). Yes when running the command as written can destroy data, change permissions or privileges, expose secrets, or disrupt the system.
- 13 category nouls: `data_destruction`, `device_overwrite`, `overbroad_permissions`, `privilege_escalation`, `remote_code_execution`, `unsafe_download_exec`, `resource_exhaustion`, `credential_exposure`, `irreversible_config`, `history_log_tampering`, `network_disruption`, `signal_kill`, `shell_footgun`.
- `reversible` and `requires_root`: nouls for undoing the effect without backups and for needing root to do the harm.

The state is `{"shell": ..., "command": ...}`, with shell one of `bash`, `zsh`, `sh`, `dash`, or `fish`. The model accepts other questions in the same schema, but it was trained and evaluated only on these 17.

## llama.cpp

`llama-server` serves the model through its `/v1/systemone` endpoint. It reads the command once and answers all 17 questions from that prefix.

```sh
hf download yannelli/cli-command-risk-GGUF d1-3b-cli-command-risk-Q8_0.gguf questions.json --local-dir .
llama-server -m d1-3b-cli-command-risk-Q8_0.gguf -c 4096
```

```python
import json
import urllib.request
questions = json.load(open("questions.json"))["questions"]
state = {"shell": "bash", "command": "dd if=/dev/zero of=/dev/sda"}
body = json.dumps({"state": state, "questions": questions}).encode()
request = urllib.request.Request("http://127.0.0.1:8080/v1/systemone", body, {"Content-Type": "application/json"})
answers = json.load(urllib.request.urlopen(request))["answers"]
print(json.dumps(answers, indent=1))
```

The answers use the System One shape: a `noul` answer is P(yes), and a `score` answer has `score` (the expected level), `probabilities` over the levels, and the `legend`. Read `probabilities` for each level's probability.

On two test-split commands, Q8_0 on CPU gives:
| Question | `dd if=/dev/zero of=/dev/sda` | Gold | `printf '%s\n' 'deploy ALL=(ALL) NOPASSWD: ALL' \| sudo tee -a /etc/sudoers` | Gold |
|---|---|---|---|---|
| `risk_level` | `critical` (0.999) | `critical` | `critical` (0.996) | `critical` |
| `risky` | P(yes) 1.00 | yes | P(yes) 1.00 | yes |
| Categories above 0.5 | `device_overwrite` 1.00, `data_destruction` 0.98 | the same two | `privilege_escalation` 0.99 | the same one |
| Other categories | P(yes) 0.001 or less | no | P(yes) 0.03 or less | no |
| `reversible` | P(yes) 0.001 | no | P(yes) 0.997 | yes |
| `requires_root` | P(yes) 1.00 | yes | P(yes) 1.00 | yes |

Tested with llama.cpp at commit `f2cc728` (2026-10-11). The GGUF files carry `lfm2.decision.type` and a `systemone` chat template, so a llama.cpp build with decision-model support serves them without extra flags.

## Ollama

The files in `ollama/` hold the same weights with the `lfm2.decision.type` key removed, because Ollama (tested with v0.40.3) refuses completion for a GGUF that carries it and its `/v1/systemone` endpoint does not support d1. `ollama_d1.py` sends d1's prompt for each question through `/api/generate` and reads the log-probabilities of the answer tokens. It uses the Python standard library only.

```sh
cd ollama
ollama create d1-3b-cli-command-risk -f Modelfile.Q8_0
python3 ollama_d1.py --model d1-3b-cli-command-risk request.json    # {"state": ..., "questions": {...}}
```

Ollama sends one request per question and cannot reuse the command's prompt cache on this hybrid convolution and attention model, so on the CPU benchmark below it takes 4.8 s per command, against 3.3 s for `llama-server`.

## Benchmarks

Answers are compared with the PyTorch model's answers (bf16). A decision agrees when its top answer is the same (P(yes) on the same side of 0.5 for nouls). The difference is the largest absolute probability gap within a decision. Latency is wall-clock time for one command with all 17 questions, one request at a time.

### CPU (16 Neoverse V3 cores)

106 test commands from 5 template families picked at random (seed 0), 1,802 decisions.

| Build | Runtime | Commands | Top answer agreement | Median diff | p95 diff | Max diff | p50 s/command |
|---|---|---:|---:|---:|---:|---:|---:|
| d1-3B Q8_0 | llama-server `/v1/systemone` | 106 | 99.9% | 0.0000 | 0.005 | 0.061 | 3.3 |
| d1-3B Q4_K_M | llama-server `/v1/systemone` | 106 | 97.9% | 0.0000 | 0.050 | 0.718 | 4.3 |
| d1-3B BF16 | llama-server `/v1/systemone` | 6 | 100% | 0.0000 | 0.001 | 0.002 | 144.4 |
| d1-3B Q8_0 | Ollama 0.40.3 + `ollama_d1.py` | 106 | 99.9% | 0.0000 | 0.005 | 0.065 | 4.8 |

BF16 runs at about 144 s per command on this CPU, so it was stopped after 6 commands. For reference, the PyTorch model takes 44 ms per command on an A100.

## Limitations

Quantization adds the differences shown in [Benchmarks](#benchmarks); Q4_K_M can flip answers close to the cutoff. The source model card covers the model's own limitations.

## License

These files are derivatives of [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B) and are released under the base model's LFM Open License v1.0, included as `LICENSE`. The license allows commercial use only for entities with less than $10M in annual revenue; read `LICENSE` for the full terms.

The training data, [CLI Command Risk](https://huggingface.co/datasets/yannelli/cli-command-risk), is CC BY 4.0.

## Citation

```bibtex
@misc{yannelli2026clicommandriskgguf,
  title        = {CLI Command Risk GGUF},
  author       = {Ryan Yannelli},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/yannelli/cli-command-risk-GGUF}}
}
@misc{yannelli2026clicommandrisk,
  title        = {CLI Command Risk},
  author       = {Ryan Yannelli},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/yannelli/cli-command-risk}}
}
```

## Maintainer

[Ryan Yannelli](https://ryanyannelli.com) | 10/19/2026 | [GitHub: @yannelli](https://github.com/yannelli) | [Hugging Face: yannelli](https://huggingface.co/yannelli)

	---
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	base_model: yannelli/d1-3b-cli-command-risk
	base_model_relation: quantized
	datasets:
	- yannelli/cli-command-risk
	language:
	- en
	library_name: gguf
	pipeline_tag: text-classification
	tags:
	- gguf
	- llama.cpp
	- ollama
	- decision-model
	- typed-decisions
	- system-one
	- shell
	- guardrail
	- command-risk
	---

	> A newer version is available: [CLI Command Risk v1.5 GGUF](https://huggingface.co/yannelli/cli-command-risk-v1.5-GGUF), trained on the dataset revision tagged [`v1.5`](https://huggingface.co/datasets/yannelli/cli-command-risk/tree/v1.5). These builds come from v1, which was trained on the revision tagged [`v1`](https://huggingface.co/datasets/yannelli/cli-command-risk/tree/v1).

	![CLI Command Risk GGUF: GGUF builds of d1-3B CLI Command Risk for llama.cpp, plus Q8_0 and Q4_K_M files for Ollama.](https://huggingface.co/yannelli/cli-command-risk-GGUF/resolve/main/assets/cli-command-risk-gguf-banner.png)

	# CLI Command Risk GGUF

	GGUF builds of a decision model that rates the risk of a shell command, for llama.cpp and Ollama. Give the model a command and its shell, and it answers 17 typed questions with probabilities: the risk level from `safe` to `critical`, whether the command is risky, which of 13 risk categories apply, whether the effect can be undone without backups, and whether the harm needs root. The model scores answer options directly and generates no text.

	The source model is [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B) fine-tuned with LoRA on [CLI Command Risk](https://huggingface.co/datasets/yannelli/cli-command-risk). Test-split results of the source model (PyTorch bf16, 12,689 commands, 215,713 decisions):

	\| Source model \| Params \| Macro F1 @ 0.5 \| Level macro F1 \| Accuracy \| ECE \| Builds here \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---\|
	\| [d1-3B CLI Command Risk](https://huggingface.co/yannelli/d1-3b-cli-command-risk) \| 3.1B \| 0.929 \| 0.669 \| 0.963 \| 0.030 \| BF16, Q8_0, Q4_K_M \|

	Macro F1 averages the 13 risk-category questions at a 0.5 cutoff. Level macro F1 averages F1 over the 5 risk levels, using the most likely level. Accuracy counts every decision. The source model card holds the full results, training details and limitations.

	## Files

	\| File \| Size \| Runtime \|
	\|---\|---:\|---\|
	\| `d1-3b-cli-command-risk-Q8_0.gguf` \| 2.9 GB \| llama.cpp `llama-server`, `/v1/systemone` \|
	\| `d1-3b-cli-command-risk-Q4_K_M.gguf` \| 1.7 GB \| llama.cpp `llama-server`, `/v1/systemone` \|
	\| `d1-3b-cli-command-risk-BF16.gguf` \| 5.4 GB \| llama.cpp `llama-server`, `/v1/systemone` \|
	\| `ollama/d1-3b-cli-command-risk-Q4_K_M.gguf` + `ollama/Modelfile` \| 1.7 GB \| Ollama, with `ollama/ollama_d1.py` \|
	\| `ollama/d1-3b-cli-command-risk-Q8_0.gguf` + `ollama/Modelfile.Q8_0` \| 2.9 GB \| Ollama, with `ollama/ollama_d1.py` \|
	\| `questions.json` \| 6 KB \| The 17 questions the model was trained on \|

	Use Q8_0. It matches the PyTorch model on 99.9% of top answers in the CPU benchmark below. Q4_K_M changes 2.1% of top answers, so use it only when memory is tight.

	## Questions

	`questions.json` holds the 17 questions in the System One schema:

	- `risk_level`: a score from 0 (`safe`) to 4 (`critical`). Each level's definition is in the question's criteria.
	- `risky`: a yes/no question (`noul`). Yes when running the command as written can destroy data, change permissions or privileges, expose secrets, or disrupt the system.
	- 13 category nouls: `data_destruction`, `device_overwrite`, `overbroad_permissions`, `privilege_escalation`, `remote_code_execution`, `unsafe_download_exec`, `resource_exhaustion`, `credential_exposure`, `irreversible_config`, `history_log_tampering`, `network_disruption`, `signal_kill`, `shell_footgun`.
	- `reversible` and `requires_root`: nouls for undoing the effect without backups and for needing root to do the harm.

	The state is `{"shell": ..., "command": ...}`, with shell one of `bash`, `zsh`, `sh`, `dash`, or `fish`. The model accepts other questions in the same schema, but it was trained and evaluated only on these 17.

	## llama.cpp

	`llama-server` serves the model through its `/v1/systemone` endpoint. It reads the command once and answers all 17 questions from that prefix.

	```sh
	hf download yannelli/cli-command-risk-GGUF d1-3b-cli-command-risk-Q8_0.gguf questions.json --local-dir .
	llama-server -m d1-3b-cli-command-risk-Q8_0.gguf -c 4096
	```

	```python
	import json
	import urllib.request

	questions = json.load(open("questions.json"))["questions"]
	state = {"shell": "bash", "command": "dd if=/dev/zero of=/dev/sda"}

	body = json.dumps({"state": state, "questions": questions}).encode()
	request = urllib.request.Request("http://127.0.0.1:8080/v1/systemone", body, {"Content-Type": "application/json"})
	answers = json.load(urllib.request.urlopen(request))["answers"]
	print(json.dumps(answers, indent=1))
	```

	The answers use the System One shape: a `noul` answer is P(yes), and a `score` answer has `score` (the expected level), `probabilities` over the levels, and the `legend`. Read `probabilities` for each level's probability.

	On two test-split commands, Q8_0 on CPU gives:

	\| Question \| `dd if=/dev/zero of=/dev/sda` \| Gold \| `printf '%s\n' 'deploy ALL=(ALL) NOPASSWD: ALL' \\| sudo tee -a /etc/sudoers` \| Gold \|
	\|---\|---\|---\|---\|---\|
	\| `risk_level` \| `critical` (0.999) \| `critical` \| `critical` (0.996) \| `critical` \|
	\| `risky` \| P(yes) 1.00 \| yes \| P(yes) 1.00 \| yes \|
	\| Categories above 0.5 \| `device_overwrite` 1.00, `data_destruction` 0.98 \| the same two \| `privilege_escalation` 0.99 \| the same one \|
	\| Other categories \| P(yes) 0.001 or less \| no \| P(yes) 0.03 or less \| no \|
	\| `reversible` \| P(yes) 0.001 \| no \| P(yes) 0.997 \| yes \|
	\| `requires_root` \| P(yes) 1.00 \| yes \| P(yes) 1.00 \| yes \|

	Tested with llama.cpp at commit `f2cc728` (2026-10-11). The GGUF files carry `lfm2.decision.type` and a `systemone` chat template, so a llama.cpp build with decision-model support serves them without extra flags.

	## Ollama

	The files in `ollama/` hold the same weights with the `lfm2.decision.type` key removed, because Ollama (tested with v0.40.3) refuses completion for a GGUF that carries it and its `/v1/systemone` endpoint does not support d1. `ollama_d1.py` sends d1's prompt for each question through `/api/generate` and reads the log-probabilities of the answer tokens. It uses the Python standard library only.

	```sh
	cd ollama
	ollama create d1-3b-cli-command-risk -f Modelfile.Q8_0
	python3 ollama_d1.py --model d1-3b-cli-command-risk request.json # {"state": ..., "questions": {...}}
	```

	Ollama sends one request per question and cannot reuse the command's prompt cache on this hybrid convolution and attention model, so on the CPU benchmark below it takes 4.8 s per command, against 3.3 s for `llama-server`.

	## Benchmarks

	Answers are compared with the PyTorch model's answers (bf16). A decision agrees when its top answer is the same (P(yes) on the same side of 0.5 for nouls). The difference is the largest absolute probability gap within a decision. Latency is wall-clock time for one command with all 17 questions, one request at a time.

	### CPU (16 Neoverse V3 cores)

	106 test commands from 5 template families picked at random (seed 0), 1,802 decisions.

	\| Build \| Runtime \| Commands \| Top answer agreement \| Median diff \| p95 diff \| Max diff \| p50 s/command \|
	\|---\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| d1-3B Q8_0 \| llama-server `/v1/systemone` \| 106 \| 99.9% \| 0.0000 \| 0.005 \| 0.061 \| 3.3 \|
	\| d1-3B Q4_K_M \| llama-server `/v1/systemone` \| 106 \| 97.9% \| 0.0000 \| 0.050 \| 0.718 \| 4.3 \|
	\| d1-3B BF16 \| llama-server `/v1/systemone` \| 6 \| 100% \| 0.0000 \| 0.001 \| 0.002 \| 144.4 \|
	\| d1-3B Q8_0 \| Ollama 0.40.3 + `ollama_d1.py` \| 106 \| 99.9% \| 0.0000 \| 0.005 \| 0.065 \| 4.8 \|

	BF16 runs at about 144 s per command on this CPU, so it was stopped after 6 commands. For reference, the PyTorch model takes 44 ms per command on an A100.

	## Limitations

	Quantization adds the differences shown in [Benchmarks](#benchmarks); Q4_K_M can flip answers close to the cutoff. The source model card covers the model's own limitations.

	## License

	These files are derivatives of [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B) and are released under the base model's LFM Open License v1.0, included as `LICENSE`. The license allows commercial use only for entities with less than $10M in annual revenue; read `LICENSE` for the full terms.

	The training data, [CLI Command Risk](https://huggingface.co/datasets/yannelli/cli-command-risk), is CC BY 4.0.

	## Citation

	```bibtex
	@misc{yannelli2026clicommandriskgguf,
	title = {CLI Command Risk GGUF},
	author = {Ryan Yannelli},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/yannelli/cli-command-risk-GGUF}}
	}

	@misc{yannelli2026clicommandrisk,
	title = {CLI Command Risk},
	author = {Ryan Yannelli},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/datasets/yannelli/cli-command-risk}}
	}
	```

	## Maintainer

	[Ryan Yannelli](https://ryanyannelli.com) \| 10/19/2026 \| [GitHub: @yannelli](https://github.com/yannelli) \| [Hugging Face: yannelli](https://huggingface.co/yannelli)