[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG4F0ADHQGPCVK3C1QRNGQ":3,"skill-same:01M6XG4F0ADHQGPCVK3C1QRNGQ":88},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":14,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6XG4F0ADHQGPCVK3C1QRNGQ","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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllava\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"for","image","pillow","conversations","image understanding","Image Chat","Visual Question Answering","Vision-Language","synthetic","vision","clip","question","analysis","tuning","Tools","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.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\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 2.0\n\n\n",{"name":5,"tags":48,"author":56,"license":15,"version":57,"category":58,"description":7,"dependencies":59},[49,31,50,30,29,51,52,53,54,55],"LLaVA","Multimodal","CLIP","Vicuna","Conversational AI","Instruction Tuning","VQA","Synthetic Sciences","1.0.0","llm-tools",[60,61,26],"transformers","torch","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllava","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add fadrienne\u002FMyGitHub --skill llava",{"tool":74,"command":75},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill llava",{"tool":77,"command":78},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill llava","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllava\u002FSKILL.md",true,[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Ftraining.md","REFERENCE","参考文档",4738,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllava\u002Freferences\u002Ftraining.md",[89,118,144,178,205,232],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":98,"publishedAt":43},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[99,100,101,102,103,104,105,106,107,108,109,36,110,111,112,113,114,115,116,117],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":127,"publishedAt":43},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[36,128,99,129,130,131,107,132,133,134,24,135,136,137,138,139,140,141,142,143],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":159,"publishedAt":43},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[150,153,154],{"categoryNo":151,"categoryName":152,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[160,161,162,163,164,165,166,167,168,169,108,170,171,172,173,174,175,176,133,177],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":188,"publishedAt":43},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[184,187],{"categoryNo":185,"categoryName":186,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},[189,161,190,191,192,193,194,195,196,168,107,197,198,199,200,201,202,203,24,204],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":217,"publishedAt":43},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":215,"categoryName":216,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000030","开发工程师",[218,219,139,220,221,222,223,106,224,107,108,36,225,226,227,228,229,230,176,231],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":236,"licenseFileUrl":6,"sourceHtmlUrl":237,"categories":238,"keywords":245,"publishedAt":43},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[239,242,243,244],{"categoryNo":240,"categoryName":241,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":151,"categoryName":152,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},[160,246,247,248,249,250,189,251,252,107,108,36,253,254,255,256,257,258,161,176],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]