[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CAFSM9NJTEXJ00K09X36EY":3,"skill-same:01M6CAFSM9NJTEXJ00K09X36EY":104},{"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":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":6,"ownerHtmlUrl":70,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":71,"install":72,"resources":85},"01M6CAFSM9NJTEXJ00K09X36EY","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.","jleechanorg\u002Fjleechan-skills","jleechanorg","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F221404966?v=4","01M6C1VJ99XG950X1DWPEBCBGX","01M6C1VJDK5YVDNT3362RT5WSB",6,"MIT","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fmlops\u002Fmodels\u002Fllava\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"for","image","orchestra","pillow","conversations","image understanding","Vicuna","Image Chat","Visual Question Answering","Vision-Language","models","vision","clip","use","question","analysis","tuning","Transformers","Llama","multimodal","tasks","research","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\ndependencies: [transformers, torch, pillow]\nmetadata:\n  hermes:\n    tags: [LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA]\n\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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llava","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fmlops\u002Fmodels\u002Fllava\u002FSKILL.md",true,[86,92,96,100],{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"code-quality-reviewer-prompt.md","REFERENCE","参考文档",1115,"https:\u002F\u002Fgithub.com\u002Fcookeyholder\u002Fusing-ai-skills\u002Fblob\u002Fmain\u002Fsubagent-driven-development\u002Fcode-quality-reviewer-prompt.md",{"path":93,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":94,"sourceHtmlUrl":95},"implementer-prompt.md",4258,"https:\u002F\u002Fgithub.com\u002Fcookeyholder\u002Fusing-ai-skills\u002Fblob\u002Fmain\u002Fsubagent-driven-development\u002Fimplementer-prompt.md",{"path":97,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Ftraining.md",4738,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fmlops\u002Fmodels\u002Fllava\u002Freferences\u002Ftraining.md",{"path":101,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":102,"sourceHtmlUrl":103},"spec-reviewer-prompt.md",1999,"https:\u002F\u002Fgithub.com\u002Fcookeyholder\u002Fusing-ai-skills\u002Fblob\u002Fmain\u002Fsubagent-driven-development\u002Fspec-reviewer-prompt.md",[105,122,143,156,170,186],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":46},"01M716Y5MTJ6GYJZ2ATXCJK8G4","instinct","Use when the user invokes \u002Finstinct or \u002Fin, or asks to \"message Instinct\", \"ask Instinct\", \"tell Instinct\", \"check Instinct\", or read replies from the configured peer assistant. iMessage first, WhatsApp Web as fallback.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Finstinct\u002FSKILL.md",[],[107,112,113,114,115,36,116,117,118,119,120,121],"user","when","replies","check","identity","whatsapp","gmail","read","message","ask",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":133,"publishedAt":46},"01M716Y4HF7FVVMJSA2CCVN5VF","hr","Compatibility alias for history-resume; produce the same fresh-state, provenance-aware resume briefing.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fhr\u002FSKILL.md",[128],{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[124,134,135,136,23,137,138,139,140,141,142],"resume","compatibility","provenance","history","produce","alias","briefing","state","fresh",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":46},"01M716Y1FFNFKFJYYKF8HQA6T8","history-resume","Resume briefing for the current repository branch or worktree using fresh state and admitted history sources.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fhistory-resume\u002FSKILL.md",[149],{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},[137,134,151,152,112,38,140,153,23,154,155,141,142],"history resume","branch","repository","worktree","sources",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"publishedAt":46},"01M716XWXJ8EPHK4RH9T14TR85","grok","Alias for the canonical grokbot browser conversation workflow; \u002Fgrok talks to the Grok bot the same way \u002Fdot talks to Dot.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fgrok\u002FSKILL.md",[],[158,163,164,165,23,166,139,167,168,169],"dot","bot","workflow","canonical","conversation","browser","grokbot",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":46},"01M70N9Q07DTE0EW5KFXMMMSJ0","codexa-bench","Compare `codexa` and normal `codex` on a fair split of comparable work by default; use `--strict` for duplicate runs of the exact same task.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcodexa-bench\u002FSKILL.md",[],[177,178,179,36,180,23,181,182,183,184,185],"codexa bench","work","split","default","duplicate","task","compare","normal","codex",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":190,"licenseFileUrl":191,"sourceHtmlUrl":192,"categories":193,"keywords":203,"publishedAt":46},"01M6C24S2X60SP8BBFGAEKPEGW","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[194,197,200],{"categoryNo":195,"categoryName":196,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":198,"categoryName":199,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":201,"categoryName":202,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000032","设计师",[204,205,206,36,207,208,209,112,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,113,228],"frontend","design","frontend design","generic","ui design","react","html","components","code","artifacts","ui","aesthetics","Production","web components","landing","applications","quality","web","css","create","examples","with","react components","web ui","pages"]