[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M0WGJMQM4GTY8M6TYC67CA":3,"skill-same:01M6M0WGJMQM4GTY8M6TYC67CA":75},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6M0WGJMQM4GTY8M6TYC67CA","llamaguard",null,"Meta's 7-8B specialized moderation model for LLM input\u002Foutput filtering. 6 safety categories - violence\u002Fhate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FSecurity-and-Compliance\u002Fllamaguard\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"LlamaGuard","Output Filtering","categories","input","self","alignment","specialized","model","Safety","NeMo Guardrails","AI Safety","planning","Input Filtering","Safety Classification","Guardrails","Meta","Content Moderation","Safety Alignment","Transformers","NeMo","2026-11-10 10:12:30",false,"---\nname: llamaguard\ndescription: Meta's 7-8B specialized moderation model for LLM input\u002Foutput filtering. 6 safety categories - violence\u002Fhate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Safety Alignment, LlamaGuard, Content Moderation, Meta, Guardrails, Safety Classification, Input Filtering, Output Filtering, AI Safety]\ndependencies: [transformers, torch, vllm]\n---\n\n# LlamaGuard - AI Content Moderation\n\n## Quick start\n\nLlamaGuard is a 7-8B parameter model specialized for content safety classification.\n\n**Installation**:\n```bash\npip install transformers torch\n# Login to HuggingFace (required)\nhuggingface-cli login\n```\n\n**Basic usage**:\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nmodel_id = \"meta-llama\u002FLlamaGuard-7b\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n\ndef moderate(chat):\n    input_ids = tokenizer.apply_chat_template(chat, return_tensors=\"pt\").to(model.device)\n    output = model.generate(input_ids=input_ids, max_new_tokens=100)\n    return tokenizer.decode(output[0], skip_special_tokens=True)\n\n# Check user input\nresult = moderate([\n    {\"role\": \"user\", \"content\": \"How do I make explosives?\"}\n])\nprint(result)\n# Output: \"unsafe\\nS3\" (Criminal Planning)\n```\n\n## Common workflows\n\n### Workflow 1: Input filtering (prompt moderation)\n\n**Check user prompts before LLM**:\n```python\ndef check_input(user_message):\n    result = moderate([{\"role\": \"user\", \"content\": user_message}])\n\n    if result.startswith(\"unsafe\"):\n        category = result.split(\"\\n\")[1]\n        return False, category  # Blocked\n    else:\n        return True, None  # Safe\n\n# Example\nsafe, category = check_input(\"How do I hack a website?\")\nif not safe:\n    print(f\"Request blocked: {category}\")\n    # Return error to user\nelse:\n    # Send to LLM\n    response = llm.generate(user_message)\n```\n\n**Safety categories**:\n- **S1**: Violence & Hate\n- **S2**: Sexual Content\n- **S3**: Guns & Illegal Weapons\n- **S4**: Regulated Substances\n- **S5**: Suicide & Self-Harm\n- **S6**: Criminal Planning\n\n### Workflow 2: Output filtering (response moderation)\n\n**Check LLM responses before showing to user**:\n```python\ndef check_output(user_message, bot_response):\n    conversation = [\n        {\"role\": \"user\", \"content\": user_message},\n        {\"role\": \"assistant\", \"content\": bot_response}\n    ]\n\n    result = moderate(conversation)\n\n    if result.startswith(\"unsafe\"):\n        category = result.split(\"\\n\")[1]\n        return False, category\n    else:\n        return True, None\n\n# Example\nuser_msg = \"Tell me about harmful substances\"\nbot_msg = llm.generate(user_msg)\n\nsafe, category = check_output(user_msg, bot_msg)\nif not safe:\n    print(f\"Response blocked: {category}\")\n    # Return generic response\n    return \"I cannot provide that information.\"\nelse:\n    return bot_msg\n```\n\n### Workflow 3: vLLM deployment (fast inference)\n\n**Production-ready serving**:\n```python\nfrom vllm import LLM, SamplingParams\n\n# Initialize vLLM\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\", tensor_parallel_size=1)\n\n# Sampling params\nsampling_params = SamplingParams(\n    temperature=0.0,  # Deterministic\n    max_tokens=100\n)\n\ndef moderate_vllm(chat):\n    # Format prompt\n    prompt = tokenizer.apply_chat_template(chat, tokenize=False)\n\n    # Generate\n    output = llm.generate([prompt], sampling_params)\n    return output[0].outputs[0].text\n\n# Batch moderation\nchats = [\n    [{\"role\": \"user\", \"content\": \"How to make bombs?\"}],\n    [{\"role\": \"user\", \"content\": \"What's the weather?\"}],\n    [{\"role\": \"user\", \"content\": \"Tell me about drugs\"}]\n]\n\nprompts = [tokenizer.apply_chat_template(c, tokenize=False) for c in chats]\nresults = llm.generate(prompts, sampling_params)\n\nfor i, result in enumerate(results):\n    print(f\"Chat {i}: {result.outputs[0].text}\")\n```\n\n**Throughput**: ~50-100 requests\u002Fsec on single A100\n\n### Workflow 4: API endpoint (FastAPI)\n\n**Serve as moderation API**:\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom vllm import LLM, SamplingParams\n\napp = FastAPI()\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\")\nsampling_params = SamplingParams(temperature=0.0, max_tokens=100)\n\nclass ModerationRequest(BaseModel):\n    messages: list  # [{\"role\": \"user\", \"content\": \"...\"}]\n\n@app.post(\"\u002Fmoderate\")\ndef moderate_endpoint(request: ModerationRequest):\n    prompt = tokenizer.apply_chat_template(request.messages, tokenize=False)\n    output = llm.generate([prompt], sampling_params)[0]\n\n    result = output.outputs[0].text\n    is_safe = result.startswith(\"safe\")\n    category = None if is_safe else result.split(\"\\n\")[1] if \"\\n\" in result else None\n\n    return {\n        \"safe\": is_safe,\n        \"category\": category,\n        \"full_output\": result\n    }\n\n# Run: uvicorn api:app --host 0.0.0.0 --port 8000\n```\n\n**Usage**:\n```bash\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fmoderate \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"How to hack?\"}]}'\n\n# Response: {\"safe\": false, \"category\": \"S6\", \"full_output\": \"unsafe\\nS6\"}\n```\n\n### Workflow 5: NeMo Guardrails integration\n\n**Use with NVIDIA Guardrails**:\n```python\nfrom nemoguardrails import RailsConfig, LLMRails\nfrom nemoguardrails.integrations.llama_guard import LlamaGuard\n\n# Configure NeMo Guardrails\nconfig = RailsConfig.from_content(\"\"\"\nmodels:\n  - type: main\n    engine: openai\n    model: gpt-4\n\nrails:\n  input:\n    flows:\n      - llamaguard check input\n  output:\n    flows:\n      - llamaguard check output\n\"\"\")\n\n# Add LlamaGuard integration\nllama_guard = LlamaGuard(model_path=\"meta-llama\u002FLlamaGuard-7b\")\nrails = LLMRails(config)\nrails.register_action(llama_guard.check_input, name=\"llamaguard check input\")\nrails.register_action(llama_guard.check_output, name=\"llamaguard check output\")\n\n# Use with automatic moderation\nresponse = rails.generate(messages=[\n    {\"role\": \"user\", \"content\": \"How do I make weapons?\"}\n])\n# Automatically blocked by LlamaGuard\n```\n\n## When to use vs alternatives\n\n**Use LlamaGuard when**:\n- Need pre-trained moderation model\n- Want high accuracy (94-95%)\n- Have GPU resources (7-8B model)\n- Need detailed safety categories\n- Building production LLM apps\n\n**Model versions**:\n- **LlamaGuard 1** (7B): Original, 6 categories\n- **LlamaGuard 2** (8B): Improved, 6 categories\n- **LlamaGuard 3** (8B): Latest (2024), enhanced\n\n**Use alternatives instead**:\n- **OpenAI Moderation API**: Simpler, API-based, free\n- **Perspective API**: Google's toxicity detection\n- **NeMo Guardrails**: More comprehensive safety framework\n- **Constitutional AI**: Training-time safety\n\n## Common issues\n\n**Issue: Model access denied**\n\nLogin to HuggingFace:\n```bash\nhuggingface-cli login\n# Enter your token\n```\n\nAccept license on model page:\nhttps:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FLlamaGuard-7b\n\n**Issue: High latency (>500ms)**\n\nUse vLLM for 10× speedup:\n```python\nfrom vllm import LLM\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\")\n# Latency: 500ms → 50ms\n```\n\nEnable tensor parallelism:\n```python\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\", tensor_parallel_size=2)\n# 2× faster on 2 GPUs\n```\n\n**Issue: False positives**\n\nUse threshold-based filtering:\n```python\n# Get probability of \"unsafe\" token\nlogits = model(..., return_dict_in_generate=True, output_scores=True)\nunsafe_prob = torch.softmax(logits.scores[0][0], dim=-1)[unsafe_token_id]\n\nif unsafe_prob > 0.9:  # High confidence threshold\n    return \"unsafe\"\nelse:\n    return \"safe\"\n```\n\n**Issue: OOM on GPU**\n\nUse 8-bit quantization:\n```python\nfrom transformers import BitsAndBytesConfig\n\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_id,\n    quantization_config=quantization_config,\n    device_map=\"auto\"\n)\n# Memory: 14GB → 7GB\n```\n\n## Advanced topics\n\n**Custom categories**: See [references\u002Fcustom-categories.md](references\u002Fcustom-categories.md) for fine-tuning LlamaGuard with domain-specific safety categories.\n\n**Performance benchmarks**: See [references\u002Fbenchmarks.md](references\u002Fbenchmarks.md) for accuracy comparison with other moderation APIs and latency optimization.\n\n**Deployment guide**: See [references\u002Fdeployment.md](references\u002Fdeployment.md) for Sagemaker, Kubernetes, and scaling strategies.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA T4\u002FA10\u002FA100\n- **VRAM**:\n  - FP16: 14GB (7B model)\n  - INT8: 7GB (quantized)\n  - INT4: 4GB (QLoRA)\n- **CPU**: Possible but slow (10× latency)\n- **Throughput**: 50-100 req\u002Fsec (A100)\n\n**Latency** (single GPU):\n- HuggingFace Transformers: 300-500ms\n- vLLM: 50-100ms\n- Batched (vLLM): 20-50ms per request\n\n## Resources\n\n- HuggingFace:\n  - V1: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FLlamaGuard-7b\n  - V2: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FMeta-Llama-Guard-2-8B\n  - V3: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FMeta-Llama-Guard-3-8B\n- Paper: https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fllama-guard-llm-based-input-output-safeguard-for-human-ai-conversations\u002F\n- Integration: vLLM, Sagemaker, NeMo Guardrails\n- Accuracy: 94.5% (prompts), 95.3% (responses)\n\n\n\n","# LlamaGuard - AI Content Moderation\n\n## Quick start\n\nLlamaGuard is a 7-8B parameter model specialized for content safety classification.\n\n**Installation**:\n```bash\npip install transformers torch\n# Login to HuggingFace (required)\nhuggingface-cli login\n```\n\n**Basic usage**:\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nmodel_id = \"meta-llama\u002FLlamaGuard-7b\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n\ndef moderate(chat):\n    input_ids = tokenizer.apply_chat_template(chat, return_tensors=\"pt\").to(model.device)\n    output = model.generate(input_ids=input_ids, max_new_tokens=100)\n    return tokenizer.decode(output[0], skip_special_tokens=True)\n\n# Check user input\nresult = moderate([\n    {\"role\": \"user\", \"content\": \"How do I make explosives?\"}\n])\nprint(result)\n# Output: \"unsafe\\nS3\" (Criminal Planning)\n```\n\n## Common workflows\n\n### Workflow 1: Input filtering (prompt moderation)\n\n**Check user prompts before LLM**:\n```python\ndef check_input(user_message):\n    result = moderate([{\"role\": \"user\", \"content\": user_message}])\n\n    if result.startswith(\"unsafe\"):\n        category = result.split(\"\\n\")[1]\n        return False, category  # Blocked\n    else:\n        return True, None  # Safe\n\n# Example\nsafe, category = check_input(\"How do I hack a website?\")\nif not safe:\n    print(f\"Request blocked: {category}\")\n    # Return error to user\nelse:\n    # Send to LLM\n    response = llm.generate(user_message)\n```\n\n**Safety categories**:\n- **S1**: Violence & Hate\n- **S2**: Sexual Content\n- **S3**: Guns & Illegal Weapons\n- **S4**: Regulated Substances\n- **S5**: Suicide & Self-Harm\n- **S6**: Criminal Planning\n\n### Workflow 2: Output filtering (response moderation)\n\n**Check LLM responses before showing to user**:\n```python\ndef check_output(user_message, bot_response):\n    conversation = [\n        {\"role\": \"user\", \"content\": user_message},\n        {\"role\": \"assistant\", \"content\": bot_response}\n    ]\n\n    result = moderate(conversation)\n\n    if result.startswith(\"unsafe\"):\n        category = result.split(\"\\n\")[1]\n        return False, category\n    else:\n        return True, None\n\n# Example\nuser_msg = \"Tell me about harmful substances\"\nbot_msg = llm.generate(user_msg)\n\nsafe, category = check_output(user_msg, bot_msg)\nif not safe:\n    print(f\"Response blocked: {category}\")\n    # Return generic response\n    return \"I cannot provide that information.\"\nelse:\n    return bot_msg\n```\n\n### Workflow 3: vLLM deployment (fast inference)\n\n**Production-ready serving**:\n```python\nfrom vllm import LLM, SamplingParams\n\n# Initialize vLLM\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\", tensor_parallel_size=1)\n\n# Sampling params\nsampling_params = SamplingParams(\n    temperature=0.0,  # Deterministic\n    max_tokens=100\n)\n\ndef moderate_vllm(chat):\n    # Format prompt\n    prompt = tokenizer.apply_chat_template(chat, tokenize=False)\n\n    # Generate\n    output = llm.generate([prompt], sampling_params)\n    return output[0].outputs[0].text\n\n# Batch moderation\nchats = [\n    [{\"role\": \"user\", \"content\": \"How to make bombs?\"}],\n    [{\"role\": \"user\", \"content\": \"What's the weather?\"}],\n    [{\"role\": \"user\", \"content\": \"Tell me about drugs\"}]\n]\n\nprompts = [tokenizer.apply_chat_template(c, tokenize=False) for c in chats]\nresults = llm.generate(prompts, sampling_params)\n\nfor i, result in enumerate(results):\n    print(f\"Chat {i}: {result.outputs[0].text}\")\n```\n\n**Throughput**: ~50-100 requests\u002Fsec on single A100\n\n### Workflow 4: API endpoint (FastAPI)\n\n**Serve as moderation API**:\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom vllm import LLM, SamplingParams\n\napp = FastAPI()\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\")\nsampling_params = SamplingParams(temperature=0.0, max_tokens=100)\n\nclass ModerationRequest(BaseModel):\n    messages: list  # [{\"role\": \"user\", \"content\": \"...\"}]\n\n@app.post(\"\u002Fmoderate\")\ndef moderate_endpoint(request: ModerationRequest):\n    prompt = tokenizer.apply_chat_template(request.messages, tokenize=False)\n    output = llm.generate([prompt], sampling_params)[0]\n\n    result = output.outputs[0].text\n    is_safe = result.startswith(\"safe\")\n    category = None if is_safe else result.split(\"\\n\")[1] if \"\\n\" in result else None\n\n    return {\n        \"safe\": is_safe,\n        \"category\": category,\n        \"full_output\": result\n    }\n\n# Run: uvicorn api:app --host 0.0.0.0 --port 8000\n```\n\n**Usage**:\n```bash\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fmoderate \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"How to hack?\"}]}'\n\n# Response: {\"safe\": false, \"category\": \"S6\", \"full_output\": \"unsafe\\nS6\"}\n```\n\n### Workflow 5: NeMo Guardrails integration\n\n**Use with NVIDIA Guardrails**:\n```python\nfrom nemoguardrails import RailsConfig, LLMRails\nfrom nemoguardrails.integrations.llama_guard import LlamaGuard\n\n# Configure NeMo Guardrails\nconfig = RailsConfig.from_content(\"\"\"\nmodels:\n  - type: main\n    engine: openai\n    model: gpt-4\n\nrails:\n  input:\n    flows:\n      - llamaguard check input\n  output:\n    flows:\n      - llamaguard check output\n\"\"\")\n\n# Add LlamaGuard integration\nllama_guard = LlamaGuard(model_path=\"meta-llama\u002FLlamaGuard-7b\")\nrails = LLMRails(config)\nrails.register_action(llama_guard.check_input, name=\"llamaguard check input\")\nrails.register_action(llama_guard.check_output, name=\"llamaguard check output\")\n\n# Use with automatic moderation\nresponse = rails.generate(messages=[\n    {\"role\": \"user\", \"content\": \"How do I make weapons?\"}\n])\n# Automatically blocked by LlamaGuard\n```\n\n## When to use vs alternatives\n\n**Use LlamaGuard when**:\n- Need pre-trained moderation model\n- Want high accuracy (94-95%)\n- Have GPU resources (7-8B model)\n- Need detailed safety categories\n- Building production LLM apps\n\n**Model versions**:\n- **LlamaGuard 1** (7B): Original, 6 categories\n- **LlamaGuard 2** (8B): Improved, 6 categories\n- **LlamaGuard 3** (8B): Latest (2024), enhanced\n\n**Use alternatives instead**:\n- **OpenAI Moderation API**: Simpler, API-based, free\n- **Perspective API**: Google's toxicity detection\n- **NeMo Guardrails**: More comprehensive safety framework\n- **Constitutional AI**: Training-time safety\n\n## Common issues\n\n**Issue: Model access denied**\n\nLogin to HuggingFace:\n```bash\nhuggingface-cli login\n# Enter your token\n```\n\nAccept license on model page:\nhttps:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FLlamaGuard-7b\n\n**Issue: High latency (>500ms)**\n\nUse vLLM for 10× speedup:\n```python\nfrom vllm import LLM\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\")\n# Latency: 500ms → 50ms\n```\n\nEnable tensor parallelism:\n```python\nllm = LLM(model=\"meta-llama\u002FLlamaGuard-7b\", tensor_parallel_size=2)\n# 2× faster on 2 GPUs\n```\n\n**Issue: False positives**\n\nUse threshold-based filtering:\n```python\n# Get probability of \"unsafe\" token\nlogits = model(..., return_dict_in_generate=True, output_scores=True)\nunsafe_prob = torch.softmax(logits.scores[0][0], dim=-1)[unsafe_token_id]\n\nif unsafe_prob > 0.9:  # High confidence threshold\n    return \"unsafe\"\nelse:\n    return \"safe\"\n```\n\n**Issue: OOM on GPU**\n\nUse 8-bit quantization:\n```python\nfrom transformers import BitsAndBytesConfig\n\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_id,\n    quantization_config=quantization_config,\n    device_map=\"auto\"\n)\n# Memory: 14GB → 7GB\n```\n\n## Advanced topics\n\n**Custom categories**: See [references\u002Fcustom-categories.md](references\u002Fcustom-categories.md) for fine-tuning LlamaGuard with domain-specific safety categories.\n\n**Performance benchmarks**: See [references\u002Fbenchmarks.md](references\u002Fbenchmarks.md) for accuracy comparison with other moderation APIs and latency optimization.\n\n**Deployment guide**: See [references\u002Fdeployment.md](references\u002Fdeployment.md) for Sagemaker, Kubernetes, and scaling strategies.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA T4\u002FA10\u002FA100\n- **VRAM**:\n  - FP16: 14GB (7B model)\n  - INT8: 7GB (quantized)\n  - INT4: 4GB (QLoRA)\n- **CPU**: Possible but slow (10× latency)\n- **Throughput**: 50-100 req\u002Fsec (A100)\n\n**Latency** (single GPU):\n- HuggingFace Transformers: 300-500ms\n- vLLM: 50-100ms\n- Batched (vLLM): 20-50ms per request\n\n## Resources\n\n- HuggingFace:\n  - V1: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FLlamaGuard-7b\n  - V2: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FMeta-Llama-Guard-2-8B\n  - V3: https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FMeta-Llama-Guard-3-8B\n- Paper: https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fllama-guard-llm-based-input-output-safeguard-for-human-ai-conversations\u002F\n- Integration: vLLM, Sagemaker, NeMo Guardrails\n- Accuracy: 94.5% (prompts), 95.3% (responses)\n\n\n\n",{"name":5,"tags":48,"author":49,"license":14,"version":50,"description":7,"dependencies":51},[40,23,39,38,37,36,35,24,33],"Orchestra Research","1.0.0",[52,53,54],"transformers","torch","vllm","main","f19b254e8975d0bfddb394686706106930d7964d","Security-and-Compliance\u002Fllamaguard","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-11 18:19:10",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add endman100\u002Fskill-router-nexus --skill llamaguard",{"tool":67,"command":68},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill llamaguard",{"tool":70,"command":71},"pnpm","pnpm dlx 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[141,142],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[144,145,146,147,148,149,150,151,152,125,153,154,155,131,156,117],"slop","stop slop","stop","drafting","when","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":161,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":43},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[159,165],"文本",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":170,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":43},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[168,174,175,176,177,178,125,179,95,130,180,181,182,183,97,184,185,186,187,148,188,189,144],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":194,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":43},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[197],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},[199,200,201,95,94,93,117,90,165,202,144,146],"humanizer","zh","humanizer zh","翻译"]