[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NBJF44M0200A3EQRVEYYGC":3,"skill-same:01M6NBJF44M0200A3EQRVEYYGC":93},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":14,"repositoryHtmlUrl":69,"repositoryHomepageUrl":6,"ownerHtmlUrl":70,"ownerBlogUrl":6,"ownerDescription":71,"updatedAt":72,"install":73,"resources":86},"01M6NBJF44M0200A3EQRVEYYGC","clip",null,"OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.","zhengliangliahu-cloud\u002Fskills","zhengliangliahu-cloud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F248268067?v=4","01M6NAQ109P2Y11XSV5D9QJFFY","Lin","01M6NAQ15R1M4E18CA2KPJHC06",1,"MIT","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F18-multimodal\u002Fclip\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"text","for","orchestra","pillow","image understanding","Vision-Language","general","vision","model","image","classification","tuning","Content Moderation","Transformers","ZeRO","multimodal","search","openai","tasks","2026-11-10 10:12:30",false,"---\nname: clip\ndescription: OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Multimodal, CLIP, Vision-Language, Zero-Shot, Image Classification, OpenAI, Image Search, Cross-Modal Retrieval, Content Moderation]\ndependencies: [transformers, torch, pillow]\n---\n\n# CLIP - Contrastive Language-Image Pre-Training\n\nOpenAI's model that understands images from natural language.\n\n## When to use CLIP\n\n**Use when:**\n- Zero-shot image classification (no training data needed)\n- Image-text similarity\u002Fmatching\n- Semantic image search\n- Content moderation (detect NSFW, violence)\n- Visual question answering\n- Cross-modal retrieval (image→text, text→image)\n\n**Metrics**:\n- **25,300+ GitHub stars**\n- Trained on 400M image-text pairs\n- Matches ResNet-50 on ImageNet (zero-shot)\n- MIT License\n\n**Use alternatives instead**:\n- **BLIP-2**: Better captioning\n- **LLaVA**: Vision-language chat\n- **Segment Anything**: Image segmentation\n\n## Quick start\n\n### Installation\n\n```bash\npip install git+https:\u002F\u002Fgithub.com\u002Fopenai\u002FCLIP.git\npip install torch torchvision ftfy regex tqdm\n```\n\n### Zero-shot classification\n\n```python\nimport torch\nimport clip\nfrom PIL import Image\n\n# Load model\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel, preprocess = clip.load(\"ViT-B\u002F32\", device=device)\n\n# Load image\nimage = preprocess(Image.open(\"photo.jpg\")).unsqueeze(0).to(device)\n\n# Define possible labels\ntext = clip.tokenize([\"a dog\", \"a cat\", \"a bird\", \"a car\"]).to(device)\n\n# Compute similarity\nwith torch.no_grad():\n    image_features = model.encode_image(image)\n    text_features = model.encode_text(text)\n\n    # Cosine similarity\n    logits_per_image, logits_per_text = model(image, text)\n    probs = logits_per_image.softmax(dim=-1).cpu().numpy()\n\n# Print results\nlabels = [\"a dog\", \"a cat\", \"a bird\", \"a car\"]\nfor label, prob in zip(labels, probs[0]):\n    print(f\"{label}: {prob:.2%}\")\n```\n\n## Available models\n\n```python\n# Models (sorted by size)\nmodels = [\n    \"RN50\",           # ResNet-50\n    \"RN101\",          # ResNet-101\n    \"ViT-B\u002F32\",       # Vision Transformer (recommended)\n    \"ViT-B\u002F16\",       # Better quality, slower\n    \"ViT-L\u002F14\",       # Best quality, slowest\n]\n\nmodel, preprocess = clip.load(\"ViT-B\u002F32\")\n```\n\n| Model | Parameters | Speed | Quality |\n|-------|------------|-------|---------|\n| RN50 | 102M | Fast | Good |\n| ViT-B\u002F32 | 151M | Medium | Better |\n| ViT-L\u002F14 | 428M | Slow | Best |\n\n## Image-text similarity\n\n```python\n# Compute embeddings\nimage_features = model.encode_image(image)\ntext_features = model.encode_text(text)\n\n# Normalize\nimage_features \u002F= image_features.norm(dim=-1, keepdim=True)\ntext_features \u002F= text_features.norm(dim=-1, keepdim=True)\n\n# Cosine similarity\nsimilarity = (image_features @ text_features.T).item()\nprint(f\"Similarity: {similarity:.4f}\")\n```\n\n## Semantic image search\n\n```python\n# Index images\nimage_paths = [\"img1.jpg\", \"img2.jpg\", \"img3.jpg\"]\nimage_embeddings = []\n\nfor img_path in image_paths:\n    image = preprocess(Image.open(img_path)).unsqueeze(0).to(device)\n    with torch.no_grad():\n        embedding = model.encode_image(image)\n        embedding \u002F= embedding.norm(dim=-1, keepdim=True)\n    image_embeddings.append(embedding)\n\nimage_embeddings = torch.cat(image_embeddings)\n\n# Search with text query\nquery = \"a sunset over the ocean\"\ntext_input = clip.tokenize([query]).to(device)\nwith torch.no_grad():\n    text_embedding = model.encode_text(text_input)\n    text_embedding \u002F= text_embedding.norm(dim=-1, keepdim=True)\n\n# Find most similar images\nsimilarities = (text_embedding @ image_embeddings.T).squeeze(0)\ntop_k = similarities.topk(3)\n\nfor idx, score in zip(top_k.indices, top_k.values):\n    print(f\"{image_paths[idx]}: {score:.3f}\")\n```\n\n## Content moderation\n\n```python\n# Define categories\ncategories = [\n    \"safe for work\",\n    \"not safe for work\",\n    \"violent content\",\n    \"graphic content\"\n]\n\ntext = clip.tokenize(categories).to(device)\n\n# Check image\nwith torch.no_grad():\n    logits_per_image, _ = model(image, text)\n    probs = logits_per_image.softmax(dim=-1)\n\n# Get classification\nmax_idx = probs.argmax().item()\nmax_prob = probs[0, max_idx].item()\n\nprint(f\"Category: {categories[max_idx]} ({max_prob:.2%})\")\n```\n\n## Batch processing\n\n```python\n# Process multiple images\nimages = [preprocess(Image.open(f\"img{i}.jpg\")) for i in range(10)]\nimages = torch.stack(images).to(device)\n\nwith torch.no_grad():\n    image_features = model.encode_image(images)\n    image_features \u002F= image_features.norm(dim=-1, keepdim=True)\n\n# Batch text\ntexts = [\"a dog\", \"a cat\", \"a bird\"]\ntext_tokens = clip.tokenize(texts).to(device)\n\nwith torch.no_grad():\n    text_features = model.encode_text(text_tokens)\n    text_features \u002F= text_features.norm(dim=-1, keepdim=True)\n\n# Similarity matrix (10 images × 3 texts)\nsimilarities = image_features @ text_features.T\nprint(similarities.shape)  # (10, 3)\n```\n\n## Integration with vector databases\n\n```python\n# Store CLIP embeddings in Chroma\u002FFAISS\nimport chromadb\n\nclient = chromadb.Client()\ncollection = client.create_collection(\"image_embeddings\")\n\n# Add image embeddings\nfor img_path, embedding in zip(image_paths, image_embeddings):\n    collection.add(\n        embeddings=[embedding.cpu().numpy().tolist()],\n        metadatas=[{\"path\": img_path}],\n        ids=[img_path]\n    )\n\n# Query with text\nquery = \"a sunset\"\ntext_embedding = model.encode_text(clip.tokenize([query]))\nresults = collection.query(\n    query_embeddings=[text_embedding.cpu().numpy().tolist()],\n    n_results=5\n)\n```\n\n## Best practices\n\n1. **Use ViT-B\u002F32 for most cases** - Good balance\n2. **Normalize embeddings** - Required for cosine similarity\n3. **Batch processing** - More efficient\n4. **Cache embeddings** - Expensive to recompute\n5. **Use descriptive labels** - Better zero-shot performance\n6. **GPU recommended** - 10-50× faster\n7. **Preprocess images** - Use provided preprocess function\n\n## Performance\n\n| Operation | CPU | GPU (V100) |\n|-----------|-----|------------|\n| Image encoding | ~200ms | ~20ms |\n| Text encoding | ~50ms | ~5ms |\n| Similarity compute | \u003C1ms | \u003C1ms |\n\n## Limitations\n\n1. **Not for fine-grained tasks** - Best for broad categories\n2. **Requires descriptive text** - Vague labels perform poorly\n3. **Biased on web data** - May have dataset biases\n4. **No bounding boxes** - Whole image only\n5. **Limited spatial understanding** - Position\u002Fcounting weak\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fopenai\u002FCLIP ⭐ 25,300+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00020\n- **Colab**: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fopenai\u002Fclip\u002F\n- **License**: MIT\n\n\n","# CLIP - Contrastive Language-Image Pre-Training\n\nOpenAI's model that understands images from natural language.\n\n## When to use CLIP\n\n**Use when:**\n- Zero-shot image classification (no training data needed)\n- Image-text similarity\u002Fmatching\n- Semantic image search\n- Content moderation (detect NSFW, violence)\n- Visual question answering\n- Cross-modal retrieval (image→text, text→image)\n\n**Metrics**:\n- **25,300+ GitHub stars**\n- Trained on 400M image-text pairs\n- Matches ResNet-50 on ImageNet (zero-shot)\n- MIT License\n\n**Use alternatives instead**:\n- **BLIP-2**: Better captioning\n- **LLaVA**: Vision-language chat\n- **Segment Anything**: Image segmentation\n\n## Quick start\n\n### Installation\n\n```bash\npip install git+https:\u002F\u002Fgithub.com\u002Fopenai\u002FCLIP.git\npip install torch torchvision ftfy regex tqdm\n```\n\n### Zero-shot classification\n\n```python\nimport torch\nimport clip\nfrom PIL import Image\n\n# Load model\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel, preprocess = clip.load(\"ViT-B\u002F32\", device=device)\n\n# Load image\nimage = preprocess(Image.open(\"photo.jpg\")).unsqueeze(0).to(device)\n\n# Define possible labels\ntext = clip.tokenize([\"a dog\", \"a cat\", \"a bird\", \"a car\"]).to(device)\n\n# Compute similarity\nwith torch.no_grad():\n    image_features = model.encode_image(image)\n    text_features = model.encode_text(text)\n\n    # Cosine similarity\n    logits_per_image, logits_per_text = model(image, text)\n    probs = logits_per_image.softmax(dim=-1).cpu().numpy()\n\n# Print results\nlabels = [\"a dog\", \"a cat\", \"a bird\", \"a car\"]\nfor label, prob in zip(labels, probs[0]):\n    print(f\"{label}: {prob:.2%}\")\n```\n\n## Available models\n\n```python\n# Models (sorted by size)\nmodels = [\n    \"RN50\",           # ResNet-50\n    \"RN101\",          # ResNet-101\n    \"ViT-B\u002F32\",       # Vision Transformer (recommended)\n    \"ViT-B\u002F16\",       # Better quality, slower\n    \"ViT-L\u002F14\",       # Best quality, slowest\n]\n\nmodel, preprocess = clip.load(\"ViT-B\u002F32\")\n```\n\n| Model | Parameters | Speed | Quality |\n|-------|------------|-------|---------|\n| RN50 | 102M | Fast | Good |\n| ViT-B\u002F32 | 151M | Medium | Better |\n| ViT-L\u002F14 | 428M | Slow | Best |\n\n## Image-text similarity\n\n```python\n# Compute embeddings\nimage_features = model.encode_image(image)\ntext_features = model.encode_text(text)\n\n# Normalize\nimage_features \u002F= image_features.norm(dim=-1, keepdim=True)\ntext_features \u002F= text_features.norm(dim=-1, keepdim=True)\n\n# Cosine similarity\nsimilarity = (image_features @ text_features.T).item()\nprint(f\"Similarity: {similarity:.4f}\")\n```\n\n## Semantic image search\n\n```python\n# Index images\nimage_paths = [\"img1.jpg\", \"img2.jpg\", \"img3.jpg\"]\nimage_embeddings = []\n\nfor img_path in image_paths:\n    image = preprocess(Image.open(img_path)).unsqueeze(0).to(device)\n    with torch.no_grad():\n        embedding = model.encode_image(image)\n        embedding \u002F= embedding.norm(dim=-1, keepdim=True)\n    image_embeddings.append(embedding)\n\nimage_embeddings = torch.cat(image_embeddings)\n\n# Search with text query\nquery = \"a sunset over the ocean\"\ntext_input = clip.tokenize([query]).to(device)\nwith torch.no_grad():\n    text_embedding = model.encode_text(text_input)\n    text_embedding \u002F= text_embedding.norm(dim=-1, keepdim=True)\n\n# Find most similar images\nsimilarities = (text_embedding @ image_embeddings.T).squeeze(0)\ntop_k = similarities.topk(3)\n\nfor idx, score in zip(top_k.indices, top_k.values):\n    print(f\"{image_paths[idx]}: {score:.3f}\")\n```\n\n## Content moderation\n\n```python\n# Define categories\ncategories = [\n    \"safe for work\",\n    \"not safe for work\",\n    \"violent content\",\n    \"graphic content\"\n]\n\ntext = clip.tokenize(categories).to(device)\n\n# Check image\nwith torch.no_grad():\n    logits_per_image, _ = model(image, text)\n    probs = logits_per_image.softmax(dim=-1)\n\n# Get classification\nmax_idx = probs.argmax().item()\nmax_prob = probs[0, max_idx].item()\n\nprint(f\"Category: {categories[max_idx]} ({max_prob:.2%})\")\n```\n\n## Batch processing\n\n```python\n# Process multiple images\nimages = [preprocess(Image.open(f\"img{i}.jpg\")) for i in range(10)]\nimages = torch.stack(images).to(device)\n\nwith torch.no_grad():\n    image_features = model.encode_image(images)\n    image_features \u002F= image_features.norm(dim=-1, keepdim=True)\n\n# Batch text\ntexts = [\"a dog\", \"a cat\", \"a bird\"]\ntext_tokens = clip.tokenize(texts).to(device)\n\nwith torch.no_grad():\n    text_features = model.encode_text(text_tokens)\n    text_features \u002F= text_features.norm(dim=-1, keepdim=True)\n\n# Similarity matrix (10 images × 3 texts)\nsimilarities = image_features @ text_features.T\nprint(similarities.shape)  # (10, 3)\n```\n\n## Integration with vector databases\n\n```python\n# Store CLIP embeddings in Chroma\u002FFAISS\nimport chromadb\n\nclient = chromadb.Client()\ncollection = client.create_collection(\"image_embeddings\")\n\n# Add image embeddings\nfor img_path, embedding in zip(image_paths, image_embeddings):\n    collection.add(\n        embeddings=[embedding.cpu().numpy().tolist()],\n        metadatas=[{\"path\": img_path}],\n        ids=[img_path]\n    )\n\n# Query with text\nquery = \"a sunset\"\ntext_embedding = model.encode_text(clip.tokenize([query]))\nresults = collection.query(\n    query_embeddings=[text_embedding.cpu().numpy().tolist()],\n    n_results=5\n)\n```\n\n## Best practices\n\n1. **Use ViT-B\u002F32 for most cases** - Good balance\n2. **Normalize embeddings** - Required for cosine similarity\n3. **Batch processing** - More efficient\n4. **Cache embeddings** - Expensive to recompute\n5. **Use descriptive labels** - Better zero-shot performance\n6. **GPU recommended** - 10-50× faster\n7. **Preprocess images** - Use provided preprocess function\n\n## Performance\n\n| Operation | CPU | GPU (V100) |\n|-----------|-----|------------|\n| Image encoding | ~200ms | ~20ms |\n| Text encoding | ~50ms | ~5ms |\n| Similarity compute | \u003C1ms | \u003C1ms |\n\n## Limitations\n\n1. **Not for fine-grained tasks** - Best for broad categories\n2. **Requires descriptive text** - Vague labels perform poorly\n3. **Biased on web data** - May have dataset biases\n4. **No bounding boxes** - Whole image only\n5. **Limited spatial understanding** - Position\u002Fcounting weak\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fopenai\u002FCLIP ⭐ 25,300+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00020\n- **Colab**: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fopenai\u002Fclip\u002F\n- **License**: MIT\n\n\n",{"name":5,"tags":53,"author":61,"license":15,"version":62,"description":7,"dependencies":63},[54,55,34,56,57,58,59,60,41],"Multimodal","CLIP","Zero-Shot","Image Classification","OpenAI","Image Search","Cross-Modal Retrieval","Orchestra Research","1.0.0",[64,65,32],"transformers","torch","main","75c7c92896e878938460f0bceb23b6ab4a787286","AI-Research-SKILLs\u002F18-multimodal\u002Fclip","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud","A postgraduate student majoring in mathematics.","2026-06-03 19:04:43",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add zhengliangliahu-cloud\u002Fskills --skill clip",{"tool":79,"command":80},"bunx","bunx skills add zhengliangliahu-cloud\u002Fskills --skill clip",{"tool":82,"command":83},"pnpm","pnpm dlx skills add zhengliangliahu-cloud\u002Fskills --skill clip","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F18-multimodal\u002Fclip\u002FSKILL.md",true,[87],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fapplications.md","REFERENCE","参考文档",5360,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F18-multimodal\u002Fclip\u002Freferences\u002Fapplications.md",[94,124,154,178,203,223],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":103,"publishedAt":48},"01M6NBV8V36BCHZ1K9F7EMRJP1","ara-rigor-reviewer","Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Frigor-reviewer\u002FSKILL.md",[100],{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123],"reviewer","ara rigor reviewer","ARA","review","release","audit","integrity","peer review","critique","exploration","scoring","dimensions","coherence","structural","scope","native","argument","epistemic","calibration","Research Artifacts",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":135,"publishedAt":48},"01M6NBTQTZ5G21FFPMEKHQZ3R3","ara-research-manager","Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara\u002F directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fresearch-manager\u002FSKILL.md",[130,133,134],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[136,137,138,139,140,141,142,143,144,145,146,147,113,148,149,150,151,152,153,106],"research","manager","ara research manager","writing","directory","heuristics","how","knowledge","knowledge management","never","scanning","conversation","maintain","project","end","recording","at","claims",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":164,"publishedAt":48},"01M6NBTCRK24WXZYKBD19RAV09","ara-compiler","Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fcompiler\u002FSKILL.md",[160,163],{"categoryNo":161,"categoryName":162,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},[165,166,167,168,169,170,141,143,171,113,172,173,119,174,175,176,153,106,123,177],"compiler","ara compiler","PDF","notes","code","logs","ingestion","artifact","input","papers","directories","layer","Knowledge Extraction",{"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":185,"publishedAt":48},"01M6NBSXGH49PE13PF1H9P7F8N","creative-thinking-for-research","Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fcreative-thinking-for-research\u002FSKILL.md",[184],{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},[136,186,187,188,189,30,190,191,192,193,194,195,196,197,198,199,200,201,31,202],"creative","thinking","Creative Thinking","creative thinking for research","reasoning","science","use","ideation","frameworks","research ideation","strategies","problem","Analogical Reasoning","Problem Reformulation","novel","directions","constraint",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":210,"publishedAt":48},"01M6NBSNHZYM70ZAFXQNESYYBG","brainstorming-research-ideas","Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fbrainstorming-research-ideas\u002FSKILL.md",[209],{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},[136,211,212,213,214,215,216,217,187,194,218,197,219,220,188,221,222,200,201,31],"brainstorming","ideas","brainstorming research ideas","discovery","projects","work","strategy","discover","impact","guides","Problem Discovery","Research Strategy",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":227,"categories":228,"keywords":235,"publishedAt":48},"01M6NBS3Z3CJYH5225X73AZN3S","systems-paper-writing","Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F20-ml-paper-writing\u002Fsystems-paper-writing\u002FSKILL.md",[229,230,231,234],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":232,"categoryName":233,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[139,236,237,238,239,104,240,241,242,117,243,244,245,246,174,247,31,30,248,249],"Paper Writing","systems","paper","Systems Paper Writing","guide","Blueprint","patterns","OSDI","NSDI","ASPLOS","SOSP","targeting","EuroSys","Structural Blueprint"]