[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KWAWXVX4QDT9CVYCPQ66CX":3,"skill-same:01M6KWAWXVX4QDT9CVYCPQ66CX":85},{"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":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":13,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":64,"install":65,"resources":78},"01M6KWAWXVX4QDT9CVYCPQ66CX","llava",null,"Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna\u002FLLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.","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\u002FImage-Recognition\u002Fllava\u002Fllava\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"vision","for","orchestra","pillow","conversations","image understanding","Vicuna","Image Chat","Visual Question Answering","Vision-Language","image","clip","question","analysis","tuning","Transformers","multimodal","tasks","assistant","2026-11-10 10:12:30",false,"---\nname: llava\ndescription: Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna\u002FLLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA]\ndependencies: [transformers, torch, pillow]\n---\n\n# LLaVA - Large Language and Vision Assistant\n\nOpen-source vision-language model for conversational image understanding.\n\n## When to use LLaVA\n\n**Use when:**\n- Building vision-language chatbots\n- Visual question answering (VQA)\n- Image description and captioning\n- Multi-turn image conversations\n- Visual instruction following\n- Document understanding with images\n\n**Metrics**:\n- **23,000+ GitHub stars**\n- GPT-4V level capabilities (targeted)\n- Apache 2.0 License\n- Multiple model sizes (7B-34B params)\n\n**Use alternatives instead**:\n- **GPT-4V**: Highest quality, API-based\n- **CLIP**: Simple zero-shot classification\n- **BLIP-2**: Better for captioning only\n- **Flamingo**: Research, not open-source\n\n## Quick start\n\n### Installation\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002Fhaotian-liu\u002FLLaVA\ncd LLaVA\n\n# Install\npip install -e .\n```\n\n### Basic usage\n\n```python\nfrom llava.model.builder import load_pretrained_model\nfrom llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token\nfrom llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN\nfrom llava.conversation import conv_templates\nfrom PIL import Image\nimport torch\n\n# Load model\nmodel_path = \"liuhaotian\u002Fllava-v1.5-7b\"\ntokenizer, model, image_processor, context_len = load_pretrained_model(\n    model_path=model_path,\n    model_base=None,\n    model_name=get_model_name_from_path(model_path)\n)\n\n# Load image\nimage = Image.open(\"image.jpg\")\nimage_tensor = process_images([image], image_processor, model.config)\nimage_tensor = image_tensor.to(model.device, dtype=torch.float16)\n\n# Create conversation\nconv = conv_templates[\"llava_v1\"].copy()\nconv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + \"\\nWhat is in this image?\")\nconv.append_message(conv.roles[1], None)\nprompt = conv.get_prompt()\n\n# Generate response\ninput_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)\n\nwith torch.inference_mode():\n    output_ids = model.generate(\n        input_ids,\n        images=image_tensor,\n        do_sample=True,\n        temperature=0.2,\n        max_new_tokens=512\n    )\n\nresponse = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()\nprint(response)\n```\n\n## Available models\n\n| Model | Parameters | VRAM | Quality |\n|-------|------------|------|---------|\n| LLaVA-v1.5-7B | 7B | ~14 GB | Good |\n| LLaVA-v1.5-13B | 13B | ~28 GB | Better |\n| LLaVA-v1.6-34B | 34B | ~70 GB | Best |\n\n```python\n# Load different models\nmodel_7b = \"liuhaotian\u002Fllava-v1.5-7b\"\nmodel_13b = \"liuhaotian\u002Fllava-v1.5-13b\"\nmodel_34b = \"liuhaotian\u002Fllava-v1.6-34b\"\n\n# 4-bit quantization for lower VRAM\nload_4bit = True  # Reduces VRAM by ~4×\n```\n\n## CLI usage\n\n```bash\n# Single image query\npython -m llava.serve.cli \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --image-file image.jpg \\\n    --query \"What is in this image?\"\n\n# Multi-turn conversation\npython -m llava.serve.cli \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --image-file image.jpg\n# Then type questions interactively\n```\n\n## Web UI (Gradio)\n\n```bash\n# Launch Gradio interface\npython -m llava.serve.gradio_web_server \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --load-4bit  # Optional: reduce VRAM\n\n# Access at http:\u002F\u002Flocalhost:7860\n```\n\n## Multi-turn conversations\n\n```python\n# Initialize conversation\nconv = conv_templates[\"llava_v1\"].copy()\n\n# Turn 1\nconv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + \"\\nWhat is in this image?\")\nconv.append_message(conv.roles[1], None)\nresponse1 = generate(conv, model, image)  # \"A dog playing in a park\"\n\n# Turn 2\nconv.messages[-1][1] = response1  # Add previous response\nconv.append_message(conv.roles[0], \"What breed is the dog?\")\nconv.append_message(conv.roles[1], None)\nresponse2 = generate(conv, model, image)  # \"Golden Retriever\"\n\n# Turn 3\nconv.messages[-1][1] = response2\nconv.append_message(conv.roles[0], \"What time of day is it?\")\nconv.append_message(conv.roles[1], None)\nresponse3 = generate(conv, model, image)\n```\n\n## Common tasks\n\n### Image captioning\n\n```python\nquestion = \"Describe this image in detail.\"\nresponse = ask(model, image, question)\n```\n\n### Visual question answering\n\n```python\nquestion = \"How many people are in the image?\"\nresponse = ask(model, image, question)\n```\n\n### Object detection (textual)\n\n```python\nquestion = \"List all the objects you can see in this image.\"\nresponse = ask(model, image, question)\n```\n\n### Scene understanding\n\n```python\nquestion = \"What is happening in this scene?\"\nresponse = ask(model, image, question)\n```\n\n### Document understanding\n\n```python\nquestion = \"What is the main topic of this document?\"\nresponse = ask(model, document_image, question)\n```\n\n## Training custom model\n\n```bash\n# Stage 1: Feature alignment (558K image-caption pairs)\nbash scripts\u002Fv1_5\u002Fpretrain.sh\n\n# Stage 2: Visual instruction tuning (150K instruction data)\nbash scripts\u002Fv1_5\u002Ffinetune.sh\n```\n\n## Quantization (reduce VRAM)\n\n```python\n# 4-bit quantization\ntokenizer, model, image_processor, context_len = load_pretrained_model(\n    model_path=\"liuhaotian\u002Fllava-v1.5-13b\",\n    model_base=None,\n    model_name=get_model_name_from_path(\"liuhaotian\u002Fllava-v1.5-13b\"),\n    load_4bit=True  # Reduces VRAM ~4×\n)\n\n# 8-bit quantization\nload_8bit=True  # Reduces VRAM ~2×\n```\n\n## Best practices\n\n1. **Start with 7B model** - Good quality, manageable VRAM\n2. **Use 4-bit quantization** - Reduces VRAM significantly\n3. **GPU required** - CPU inference extremely slow\n4. **Clear prompts** - Specific questions get better answers\n5. **Multi-turn conversations** - Maintain conversation context\n6. **Temperature 0.2-0.7** - Balance creativity\u002Fconsistency\n7. **max_new_tokens 512-1024** - For detailed responses\n8. **Batch processing** - Process multiple images sequentially\n\n## Performance\n\n| Model | VRAM (FP16) | VRAM (4-bit) | Speed (tokens\u002Fs) |\n|-------|-------------|--------------|------------------|\n| 7B | ~14 GB | ~4 GB | ~20 |\n| 13B | ~28 GB | ~8 GB | ~12 |\n| 34B | ~70 GB | ~18 GB | ~5 |\n\n*On A100 GPU*\n\n## Benchmarks\n\nLLaVA achieves competitive scores on:\n- **VQAv2**: 78.5%\n- **GQA**: 62.0%\n- **MM-Vet**: 35.4%\n- **MMBench**: 64.3%\n\n## Limitations\n\n1. **Hallucinations** - May describe things not in image\n2. **Spatial reasoning** - Struggles with precise locations\n3. **Small text** - Difficulty reading fine print\n4. **Object counting** - Imprecise for many objects\n5. **VRAM requirements** - Need powerful GPU\n6. **Inference speed** - Slower than CLIP\n\n## Integration with frameworks\n\n### LangChain\n\n```python\nfrom langchain.llms.base import LLM\n\nclass LLaVALLM(LLM):\n    def _call(self, prompt, stop=None):\n        # Custom LLaVA inference\n        return response\n\nllm = LLaVALLM()\n```\n\n### Gradio App\n\n```python\nimport gradio as gr\n\ndef chat(image, text, history):\n    response = ask_llava(model, image, text)\n    return response\n\ndemo = gr.ChatInterface(\n    chat,\n    additional_inputs=[gr.Image(type=\"pil\")],\n    title=\"LLaVA Chat\"\n)\ndemo.launch()\n```\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fhaotian-liu\u002FLLaVA ⭐ 23,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.08485\n- **Demo**: https:\u002F\u002Fllava.hliu.cc\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fliuhaotian\n- **License**: Apache 2.0\n\n\n","# LLaVA - Large Language and Vision Assistant\n\nOpen-source vision-language model for conversational image understanding.\n\n## When to use LLaVA\n\n**Use when:**\n- Building vision-language chatbots\n- Visual question answering (VQA)\n- Image description and captioning\n- Multi-turn image conversations\n- Visual instruction following\n- Document understanding with images\n\n**Metrics**:\n- **23,000+ GitHub stars**\n- GPT-4V level capabilities (targeted)\n- Apache 2.0 License\n- Multiple model sizes (7B-34B params)\n\n**Use alternatives instead**:\n- **GPT-4V**: Highest quality, API-based\n- **CLIP**: Simple zero-shot classification\n- **BLIP-2**: Better for captioning only\n- **Flamingo**: Research, not open-source\n\n## Quick start\n\n### Installation\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002Fhaotian-liu\u002FLLaVA\ncd LLaVA\n\n# Install\npip install -e .\n```\n\n### Basic usage\n\n```python\nfrom llava.model.builder import load_pretrained_model\nfrom llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token\nfrom llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN\nfrom llava.conversation import conv_templates\nfrom PIL import Image\nimport torch\n\n# Load model\nmodel_path = \"liuhaotian\u002Fllava-v1.5-7b\"\ntokenizer, model, image_processor, context_len = load_pretrained_model(\n    model_path=model_path,\n    model_base=None,\n    model_name=get_model_name_from_path(model_path)\n)\n\n# Load image\nimage = Image.open(\"image.jpg\")\nimage_tensor = process_images([image], image_processor, model.config)\nimage_tensor = image_tensor.to(model.device, dtype=torch.float16)\n\n# Create conversation\nconv = conv_templates[\"llava_v1\"].copy()\nconv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + \"\\nWhat is in this image?\")\nconv.append_message(conv.roles[1], None)\nprompt = conv.get_prompt()\n\n# Generate response\ninput_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)\n\nwith torch.inference_mode():\n    output_ids = model.generate(\n        input_ids,\n        images=image_tensor,\n        do_sample=True,\n        temperature=0.2,\n        max_new_tokens=512\n    )\n\nresponse = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()\nprint(response)\n```\n\n## Available models\n\n| Model | Parameters | VRAM | Quality |\n|-------|------------|------|---------|\n| LLaVA-v1.5-7B | 7B | ~14 GB | Good |\n| LLaVA-v1.5-13B | 13B | ~28 GB | Better |\n| LLaVA-v1.6-34B | 34B | ~70 GB | Best |\n\n```python\n# Load different models\nmodel_7b = \"liuhaotian\u002Fllava-v1.5-7b\"\nmodel_13b = \"liuhaotian\u002Fllava-v1.5-13b\"\nmodel_34b = \"liuhaotian\u002Fllava-v1.6-34b\"\n\n# 4-bit quantization for lower VRAM\nload_4bit = True  # Reduces VRAM by ~4×\n```\n\n## CLI usage\n\n```bash\n# Single image query\npython -m llava.serve.cli \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --image-file image.jpg \\\n    --query \"What is in this image?\"\n\n# Multi-turn conversation\npython -m llava.serve.cli \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --image-file image.jpg\n# Then type questions interactively\n```\n\n## Web UI (Gradio)\n\n```bash\n# Launch Gradio interface\npython -m llava.serve.gradio_web_server \\\n    --model-path liuhaotian\u002Fllava-v1.5-7b \\\n    --load-4bit  # Optional: reduce VRAM\n\n# Access at http:\u002F\u002Flocalhost:7860\n```\n\n## Multi-turn conversations\n\n```python\n# Initialize conversation\nconv = conv_templates[\"llava_v1\"].copy()\n\n# Turn 1\nconv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + \"\\nWhat is in this image?\")\nconv.append_message(conv.roles[1], None)\nresponse1 = generate(conv, model, image)  # \"A dog playing in a park\"\n\n# Turn 2\nconv.messages[-1][1] = response1  # Add previous response\nconv.append_message(conv.roles[0], \"What breed is the dog?\")\nconv.append_message(conv.roles[1], None)\nresponse2 = generate(conv, model, image)  # \"Golden Retriever\"\n\n# Turn 3\nconv.messages[-1][1] = response2\nconv.append_message(conv.roles[0], \"What time of day is it?\")\nconv.append_message(conv.roles[1], None)\nresponse3 = generate(conv, model, image)\n```\n\n## Common tasks\n\n### Image captioning\n\n```python\nquestion = \"Describe this image in detail.\"\nresponse = ask(model, image, question)\n```\n\n### Visual question answering\n\n```python\nquestion = \"How many people are in the image?\"\nresponse = ask(model, image, question)\n```\n\n### Object detection (textual)\n\n```python\nquestion = \"List all the objects you can see in this image.\"\nresponse = ask(model, image, question)\n```\n\n### Scene understanding\n\n```python\nquestion = \"What is happening in this scene?\"\nresponse = ask(model, image, question)\n```\n\n### Document understanding\n\n```python\nquestion = \"What is the main topic of this document?\"\nresponse = ask(model, document_image, question)\n```\n\n## Training custom model\n\n```bash\n# Stage 1: Feature alignment (558K image-caption pairs)\nbash scripts\u002Fv1_5\u002Fpretrain.sh\n\n# Stage 2: Visual instruction tuning (150K instruction data)\nbash scripts\u002Fv1_5\u002Ffinetune.sh\n```\n\n## Quantization (reduce VRAM)\n\n```python\n# 4-bit quantization\ntokenizer, model, image_processor, context_len = load_pretrained_model(\n    model_path=\"liuhaotian\u002Fllava-v1.5-13b\",\n    model_base=None,\n    model_name=get_model_name_from_path(\"liuhaotian\u002Fllava-v1.5-13b\"),\n    load_4bit=True  # Reduces VRAM ~4×\n)\n\n# 8-bit quantization\nload_8bit=True  # Reduces VRAM ~2×\n```\n\n## Best practices\n\n1. **Start with 7B model** - Good quality, manageable VRAM\n2. **Use 4-bit quantization** - Reduces VRAM significantly\n3. **GPU required** - CPU inference extremely slow\n4. **Clear prompts** - Specific questions get better answers\n5. **Multi-turn conversations** - Maintain conversation context\n6. **Temperature 0.2-0.7** - Balance creativity\u002Fconsistency\n7. **max_new_tokens 512-1024** - For detailed responses\n8. **Batch processing** - Process multiple images sequentially\n\n## Performance\n\n| Model | VRAM (FP16) | VRAM (4-bit) | Speed (tokens\u002Fs) |\n|-------|-------------|--------------|------------------|\n| 7B | ~14 GB | ~4 GB | ~20 |\n| 13B | ~28 GB | ~8 GB | ~12 |\n| 34B | ~70 GB | ~18 GB | ~5 |\n\n*On A100 GPU*\n\n## Benchmarks\n\nLLaVA achieves competitive scores on:\n- **VQAv2**: 78.5%\n- **GQA**: 62.0%\n- **MM-Vet**: 35.4%\n- **MMBench**: 64.3%\n\n## Limitations\n\n1. **Hallucinations** - May describe things not in image\n2. **Spatial reasoning** - Struggles with precise locations\n3. **Small text** - Difficulty reading fine print\n4. **Object counting** - Imprecise for many objects\n5. **VRAM requirements** - Need powerful GPU\n6. **Inference speed** - Slower than CLIP\n\n## Integration with frameworks\n\n### LangChain\n\n```python\nfrom langchain.llms.base import LLM\n\nclass LLaVALLM(LLM):\n    def _call(self, prompt, stop=None):\n        # Custom LLaVA inference\n        return response\n\nllm = LLaVALLM()\n```\n\n### Gradio App\n\n```python\nimport gradio as gr\n\ndef chat(image, text, history):\n    response = ask_llava(model, image, text)\n    return response\n\ndemo = gr.ChatInterface(\n    chat,\n    additional_inputs=[gr.Image(type=\"pil\")],\n    title=\"LLaVA Chat\"\n)\ndemo.launch()\n```\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fhaotian-liu\u002FLLaVA ⭐ 23,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.08485\n- **Demo**: https:\u002F\u002Fllava.hliu.cc\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fliuhaotian\n- **License**: Apache 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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",[151,152],{"categoryNo":118,"categoryName":119,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":124,"categoryName":125,"taxonomyType":96,"taxonomyTypeDesc":97},[154,155,156,157,158,159,160,161,162,137,163,164,165,24,166,129],"slop","stop slop","stop","drafting","when","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":42},"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",[],[169,175],"文本",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":42},"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",[],[178,184,185,186,187,188,137,189,107,141,190,191,192,193,109,194,195,196,197,158,198,199,154],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":204,"sourceHtmlUrl":205,"categories":206,"keywords":208,"publishedAt":42},"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",[207],{"categoryNo":118,"categoryName":119,"taxonomyType":20,"taxonomyTypeDesc":21},[209,210,211,107,106,105,129,102,175,212,154,156],"humanizer","zh","humanizer zh","翻译"]