llamaguard SKILL.md skillA
llamaguard from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add orchestra-research/ai-research-skills/llamaguard@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- orchestra-research/ai-research-skills
- Path
07-safety-alignment/llamaguard/SKILL.md- Versions recorded
- 5
- Latest version
- v1.0.0, committed Nov 20, 2025
- First version recorded
- Nov 19, 2025
- Size
- 9,204 bytes
- Repository stars
- 13,718
- License
- MIT
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# LlamaGuard - AI Content Moderation
## Quick start
LlamaGuard is a 7-8B parameter model specialized for content safety classification.
**Installation**:
```bash
pip install transformers torch
# Login to HuggingFace (required)
huggingface-cli login
```
**Basic usage**:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "meta-llama/LlamaGuard-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
def moderate(chat):
input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt").to(model.device)
output = model.generate(input_ids=input_ids, max_new_tokens=100)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Check user input
result = moderate([
{"role": "user", "content": "How do I make explosives?"}
])
print(result)
# Output: "unsafe\nS3" (Criminal Planning)
```
## Common workflows
### Workflow 1: Input filtering (prompt moderation)
**Check user prompts before LLM**:
```python
def check_input(user_message):
result = moderate([{"role": "user", "content": user_message}])
if result.startswith("unsafe"):
…Read the whole file at its exact version.
How to install
mdr add orchestra-research/ai-research-skills/llamaguard@sha256:80c5afb39cb153d8The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_4b2olemuutcsg7k7)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Nov 19, 2025 and Nov 20, 2025, more than one a day.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2025-11-20 | 0a9ded4 | 9,204 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 72c8b2c | 9,194 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 85a6d14 | 9,193 B | A | view · diff |
| v1.0.0 | 2025-11-20 | f38509b | 9,192 B | A | view · diff |
| v1.0.0 | 2025-11-19 | bd4461f | 9,191 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (9204 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_4b2olemuutcsg7k7 GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/llamaguard GET https://markdownregistry.com/api/v1/blob/80c5afb39cb153d82e08dca44fbd3ea75a5e950b8bef65c2c1af8bbbe9ef3992
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.