[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EDSM27R9DABYQ32VVZ7J9A":3,"skill-same:01M6EDSM27R9DABYQ32VVZ7J9A":92},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6EDSM27R9DABYQ32VVZ7J9A","ray-data",null,"Scalable data processing for ML workloads. Streaming execution across CPU\u002FGPU, supports Parquet\u002FCSV\u002FJSON\u002Fimages. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fray-data\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"Ray Data","data","Ray","images","Batch Inference","ML Pipelines","Scalable","CPU","processing","machine","preprocessing","across","tensorflow","use","execution","pandas","streaming","batch","etl","pytorch","2026-11-10 10:12:30",false,"---\nname: ray-data\ndescription: Scalable data processing for ML workloads. Streaming execution across CPU\u002FGPU, supports Parquet\u002FCSV\u002FJSON\u002Fimages. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.\nversion: 1.0.0\nauthor: luo-kai\nlicense: MIT\ntags: [Data Processing, Ray Data, Distributed Computing, ML Pipelines, Batch Inference, ETL, Scalable, Ray, PyTorch, TensorFlow]\ndependencies: [\"ray[data]\", pyarrow, pandas]\n---\n\n# Ray Data - Scalable ML Data Processing\n\nDistributed data processing library for ML and AI workloads.\n\n## When to use Ray Data\n\n**Use Ray Data when:**\n- Processing large datasets (>100GB) for ML training\n- Need distributed data preprocessing across cluster\n- Building batch inference pipelines\n- Loading multi-modal data (images, audio, video)\n- Scaling data processing from laptop to cluster\n\n**Key features**:\n- **Streaming execution**: Process data larger than memory\n- **GPU support**: Accelerate transforms with GPUs\n- **Framework integration**: PyTorch, TensorFlow, HuggingFace\n- **Multi-modal**: Images, Parquet, CSV, JSON, audio, video\n\n**Use alternatives instead**:\n- **Pandas**: Small data (\u003C1GB) on single machine\n- **Dask**: Tabular data, SQL-like operations\n- **Spark**: Enterprise ETL, SQL queries\n\n## Quick start\n\n### Installation\n\n```bash\npip install -U 'ray[data]'\n```\n\n### Load and transform data\n\n```python\nimport ray\n\n# Read Parquet files\nds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.parquet\")\n\n# Transform data (lazy execution)\nds = ds.map_batches(lambda batch: {\"processed\": batch[\"text\"].str.lower()})\n\n# Consume data\nfor batch in ds.iter_batches(batch_size=100):\n    print(batch)\n```\n\n### Integration with Ray Train\n\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\n\n# Create dataset\ntrain_ds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Ftrain\u002F*.parquet\")\n\ndef train_func(config):\n    # Access dataset in training\n    train_ds = ray.train.get_dataset_shard(\"train\")\n\n    for epoch in range(10):\n        for batch in train_ds.iter_batches(batch_size=32):\n            # Train on batch\n            pass\n\n# Train with Ray\ntrainer = TorchTrainer(\n    train_func,\n    datasets={\"train\": train_ds},\n    scaling_config=ScalingConfig(num_workers=4, use_gpu=True)\n)\ntrainer.fit()\n```\n\n## Reading data\n\n### From cloud storage\n\n```python\nimport ray\n\n# Parquet (recommended for ML)\nds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.parquet\")\n\n# CSV\nds = ray.data.read_csv(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.csv\")\n\n# JSON\nds = ray.data.read_json(\"gs:\u002F\u002Fbucket\u002Fdata\u002F*.json\")\n\n# Images\nds = ray.data.read_images(\"s3:\u002F\u002Fbucket\u002Fimages\u002F\")\n```\n\n### From Python objects\n\n```python\n# From list\nds = ray.data.from_items([{\"id\": i, \"value\": i * 2} for i in range(1000)])\n\n# From range\nds = ray.data.range(1000000)  # Synthetic data\n\n# From pandas\nimport pandas as pd\ndf = pd.DataFrame({\"col1\": [1, 2, 3], \"col2\": [4, 5, 6]})\nds = ray.data.from_pandas(df)\n```\n\n## Transformations\n\n### Map batches (vectorized)\n\n```python\n# Batch transformation (fast)\ndef process_batch(batch):\n    batch[\"doubled\"] = batch[\"value\"] * 2\n    return batch\n\nds = ds.map_batches(process_batch, batch_size=1000)\n```\n\n### Row transformations\n\n```python\n# Row-by-row (slower)\ndef process_row(row):\n    row[\"squared\"] = row[\"value\"] ** 2\n    return row\n\nds = ds.map(process_row)\n```\n\n### Filter\n\n```python\n# Filter rows\nds = ds.filter(lambda row: row[\"value\"] > 100)\n```\n\n### Group by and aggregate\n\n```python\n# Group by column\nds = ds.groupby(\"category\").count()\n\n# Custom aggregation\nds = ds.groupby(\"category\").map_groups(lambda group: {\"sum\": group[\"value\"].sum()})\n```\n\n## GPU-accelerated transforms\n\n```python\n# Use GPU for preprocessing\ndef preprocess_images_gpu(batch):\n    import torch\n    images = torch.tensor(batch[\"image\"]).cuda()\n    # GPU preprocessing\n    processed = images * 255\n    return {\"processed\": processed.cpu().numpy()}\n\nds = ds.map_batches(\n    preprocess_images_gpu,\n    batch_size=64,\n    num_gpus=1  # Request GPU\n)\n```\n\n## Writing data\n\n```python\n# Write to Parquet\nds.write_parquet(\"s3:\u002F\u002Fbucket\u002Foutput\u002F\")\n\n# Write to CSV\nds.write_csv(\"output\u002F\")\n\n# Write to JSON\nds.write_json(\"output\u002F\")\n```\n\n## Performance optimization\n\n### Repartition\n\n```python\n# Control parallelism\nds = ds.repartition(100)  # 100 blocks for 100-core cluster\n```\n\n### Batch size tuning\n\n```python\n# Larger batches = faster vectorized ops\nds.map_batches(process_fn, batch_size=10000)  # vs batch_size=100\n```\n\n### Streaming execution\n\n```python\n# Process data larger than memory\nds = ray.data.read_parquet(\"s3:\u002F\u002Fhuge-dataset\u002F\")\nfor batch in ds.iter_batches(batch_size=1000):\n    process(batch)  # Streamed, not loaded to memory\n```\n\n## Common patterns\n\n### Batch inference\n\n```python\nimport ray\n\n# Load model\ndef load_model():\n    # Load once per worker\n    return MyModel()\n\n# Inference function\nclass BatchInference:\n    def __init__(self):\n        self.model = load_model()\n\n    def __call__(self, batch):\n        predictions = self.model(batch[\"input\"])\n        return {\"prediction\": predictions}\n\n# Run distributed inference\nds = ray.data.read_parquet(\"s3:\u002F\u002Fdata\u002F\")\npredictions = ds.map_batches(BatchInference, batch_size=32, num_gpus=1)\npredictions.write_parquet(\"s3:\u002F\u002Foutput\u002F\")\n```\n\n### Data preprocessing pipeline\n\n```python\n# Multi-step pipeline\nds = (\n    ray.data.read_parquet(\"s3:\u002F\u002Fraw\u002F\")\n    .map_batches(clean_data)\n    .map_batches(tokenize)\n    .map_batches(augment)\n    .write_parquet(\"s3:\u002F\u002Fprocessed\u002F\")\n)\n```\n\n## Integration with ML frameworks\n\n### PyTorch\n\n```python\n# Convert to PyTorch\ntorch_ds = ds.to_torch(label_column=\"label\", batch_size=32)\n\nfor batch in torch_ds:\n    # batch is dict with tensors\n    inputs, labels = batch[\"features\"], batch[\"label\"]\n```\n\n### TensorFlow\n\n```python\n# Convert to TensorFlow\ntf_ds = ds.to_tf(feature_columns=[\"image\"], label_column=\"label\", batch_size=32)\n\nfor features, labels in tf_ds:\n    # Train model\n    pass\n```\n\n## Supported data formats\n\n| Format | Read | Write | Use Case |\n|--------|------|-------|----------|\n| Parquet | ✅ | ✅ | ML data (recommended) |\n| CSV | ✅ | ✅ | Tabular data |\n| JSON | ✅ | ✅ | Semi-structured |\n| Images | ✅ | ❌ | Computer vision |\n| NumPy | ✅ | ✅ | Arrays |\n| Pandas | ✅ | ❌ | DataFrames |\n\n## Performance benchmarks\n\n**Scaling** (processing 100GB data):\n- 1 node (16 cores): ~30 minutes\n- 4 nodes (64 cores): ~8 minutes\n- 16 nodes (256 cores): ~2 minutes\n\n**GPU acceleration** (image preprocessing):\n- CPU only: 1,000 images\u002Fsec\n- 1 GPU: 5,000 images\u002Fsec\n- 4 GPUs: 18,000 images\u002Fsec\n\n## Use cases\n\n**Production deployments**:\n- **Pinterest**: Last-mile data processing for model training\n- **ByteDance**: Scaling offline inference with multi-modal LLMs\n- **Spotify**: ML platform for batch inference\n\n## References\n\n- **[Transformations Guide](references\u002Ftransformations.md)** - Map, filter, groupby operations\n- **[Integration Guide](references\u002Fintegration.md)** - Ray Train, PyTorch, TensorFlow\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fdata\u002Fdata.html\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray ⭐ 36,000+\n- **Version**: Ray 2.40.0+\n- **Examples**: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fdata\u002Fexamples\u002Foverview.html\n\n\n\n","# Ray Data - Scalable ML Data Processing\n\nDistributed data processing library for ML and AI workloads.\n\n## When to use Ray Data\n\n**Use Ray Data when:**\n- Processing large datasets (>100GB) for ML training\n- Need distributed data preprocessing across cluster\n- Building batch inference pipelines\n- Loading multi-modal data (images, audio, video)\n- Scaling data processing from laptop to cluster\n\n**Key features**:\n- **Streaming execution**: Process data larger than memory\n- **GPU support**: Accelerate transforms with GPUs\n- **Framework integration**: PyTorch, TensorFlow, HuggingFace\n- **Multi-modal**: Images, Parquet, CSV, JSON, audio, video\n\n**Use alternatives instead**:\n- **Pandas**: Small data (\u003C1GB) on single machine\n- **Dask**: Tabular data, SQL-like operations\n- **Spark**: Enterprise ETL, SQL queries\n\n## Quick start\n\n### Installation\n\n```bash\npip install -U 'ray[data]'\n```\n\n### Load and transform data\n\n```python\nimport ray\n\n# Read Parquet files\nds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.parquet\")\n\n# Transform data (lazy execution)\nds = ds.map_batches(lambda batch: {\"processed\": batch[\"text\"].str.lower()})\n\n# Consume data\nfor batch in ds.iter_batches(batch_size=100):\n    print(batch)\n```\n\n### Integration with Ray Train\n\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\n\n# Create dataset\ntrain_ds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Ftrain\u002F*.parquet\")\n\ndef train_func(config):\n    # Access dataset in training\n    train_ds = ray.train.get_dataset_shard(\"train\")\n\n    for epoch in range(10):\n        for batch in train_ds.iter_batches(batch_size=32):\n            # Train on batch\n            pass\n\n# Train with Ray\ntrainer = TorchTrainer(\n    train_func,\n    datasets={\"train\": train_ds},\n    scaling_config=ScalingConfig(num_workers=4, use_gpu=True)\n)\ntrainer.fit()\n```\n\n## Reading data\n\n### From cloud storage\n\n```python\nimport ray\n\n# Parquet (recommended for ML)\nds = ray.data.read_parquet(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.parquet\")\n\n# CSV\nds = ray.data.read_csv(\"s3:\u002F\u002Fbucket\u002Fdata\u002F*.csv\")\n\n# JSON\nds = ray.data.read_json(\"gs:\u002F\u002Fbucket\u002Fdata\u002F*.json\")\n\n# Images\nds = ray.data.read_images(\"s3:\u002F\u002Fbucket\u002Fimages\u002F\")\n```\n\n### From Python objects\n\n```python\n# From list\nds = ray.data.from_items([{\"id\": i, \"value\": i * 2} for i in range(1000)])\n\n# From range\nds = ray.data.range(1000000)  # Synthetic data\n\n# From pandas\nimport pandas as pd\ndf = pd.DataFrame({\"col1\": [1, 2, 3], \"col2\": [4, 5, 6]})\nds = ray.data.from_pandas(df)\n```\n\n## Transformations\n\n### Map batches (vectorized)\n\n```python\n# Batch transformation (fast)\ndef process_batch(batch):\n    batch[\"doubled\"] = batch[\"value\"] * 2\n    return batch\n\nds = ds.map_batches(process_batch, batch_size=1000)\n```\n\n### Row transformations\n\n```python\n# Row-by-row (slower)\ndef process_row(row):\n    row[\"squared\"] = row[\"value\"] ** 2\n    return row\n\nds = ds.map(process_row)\n```\n\n### Filter\n\n```python\n# Filter rows\nds = ds.filter(lambda row: row[\"value\"] > 100)\n```\n\n### Group by and aggregate\n\n```python\n# Group by column\nds = ds.groupby(\"category\").count()\n\n# Custom aggregation\nds = ds.groupby(\"category\").map_groups(lambda group: {\"sum\": group[\"value\"].sum()})\n```\n\n## GPU-accelerated transforms\n\n```python\n# Use GPU for preprocessing\ndef preprocess_images_gpu(batch):\n    import torch\n    images = torch.tensor(batch[\"image\"]).cuda()\n    # GPU preprocessing\n    processed = images * 255\n    return {\"processed\": processed.cpu().numpy()}\n\nds = ds.map_batches(\n    preprocess_images_gpu,\n    batch_size=64,\n    num_gpus=1  # Request GPU\n)\n```\n\n## Writing data\n\n```python\n# Write to Parquet\nds.write_parquet(\"s3:\u002F\u002Fbucket\u002Foutput\u002F\")\n\n# Write to CSV\nds.write_csv(\"output\u002F\")\n\n# Write to JSON\nds.write_json(\"output\u002F\")\n```\n\n## Performance optimization\n\n### Repartition\n\n```python\n# Control parallelism\nds = ds.repartition(100)  # 100 blocks for 100-core cluster\n```\n\n### Batch size tuning\n\n```python\n# Larger batches = faster vectorized ops\nds.map_batches(process_fn, batch_size=10000)  # vs batch_size=100\n```\n\n### Streaming execution\n\n```python\n# Process data larger than memory\nds = ray.data.read_parquet(\"s3:\u002F\u002Fhuge-dataset\u002F\")\nfor batch in ds.iter_batches(batch_size=1000):\n    process(batch)  # Streamed, not loaded to memory\n```\n\n## Common patterns\n\n### Batch inference\n\n```python\nimport ray\n\n# Load model\ndef load_model():\n    # Load once per worker\n    return MyModel()\n\n# Inference function\nclass BatchInference:\n    def __init__(self):\n        self.model = load_model()\n\n    def __call__(self, batch):\n        predictions = self.model(batch[\"input\"])\n        return {\"prediction\": predictions}\n\n# Run distributed inference\nds = ray.data.read_parquet(\"s3:\u002F\u002Fdata\u002F\")\npredictions = ds.map_batches(BatchInference, batch_size=32, num_gpus=1)\npredictions.write_parquet(\"s3:\u002F\u002Foutput\u002F\")\n```\n\n### Data preprocessing pipeline\n\n```python\n# Multi-step pipeline\nds = (\n    ray.data.read_parquet(\"s3:\u002F\u002Fraw\u002F\")\n    .map_batches(clean_data)\n    .map_batches(tokenize)\n    .map_batches(augment)\n    .write_parquet(\"s3:\u002F\u002Fprocessed\u002F\")\n)\n```\n\n## Integration with ML frameworks\n\n### PyTorch\n\n```python\n# Convert to PyTorch\ntorch_ds = ds.to_torch(label_column=\"label\", batch_size=32)\n\nfor batch in torch_ds:\n    # batch is dict with tensors\n    inputs, labels = batch[\"features\"], batch[\"label\"]\n```\n\n### TensorFlow\n\n```python\n# Convert to TensorFlow\ntf_ds = ds.to_tf(feature_columns=[\"image\"], label_column=\"label\", batch_size=32)\n\nfor features, labels in tf_ds:\n    # Train model\n    pass\n```\n\n## Supported data formats\n\n| Format | Read | Write | Use Case |\n|--------|------|-------|----------|\n| Parquet | ✅ | ✅ | ML data (recommended) |\n| CSV | ✅ | ✅ | Tabular data |\n| JSON | ✅ | ✅ | Semi-structured |\n| Images | ✅ | ❌ | Computer vision |\n| NumPy | ✅ | ✅ | Arrays |\n| Pandas | ✅ | ❌ | DataFrames |\n\n## Performance benchmarks\n\n**Scaling** (processing 100GB data):\n- 1 node (16 cores): ~30 minutes\n- 4 nodes (64 cores): ~8 minutes\n- 16 nodes (256 cores): ~2 minutes\n\n**GPU acceleration** (image preprocessing):\n- CPU only: 1,000 images\u002Fsec\n- 1 GPU: 5,000 images\u002Fsec\n- 4 GPUs: 18,000 images\u002Fsec\n\n## Use cases\n\n**Production deployments**:\n- **Pinterest**: Last-mile data processing for model training\n- **ByteDance**: Scaling offline inference with multi-modal LLMs\n- **Spotify**: ML platform for batch inference\n\n## References\n\n- **[Transformations Guide](references\u002Ftransformations.md)** - Map, filter, groupby operations\n- **[Integration Guide](references\u002Fintegration.md)** - Ray Train, PyTorch, TensorFlow\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fdata\u002Fdata.html\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray ⭐ 36,000+\n- **Version**: Ray 2.40.0+\n- **Examples**: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fdata\u002Fexamples\u002Foverview.html\n\n\n\n",{"name":5,"tags":49,"author":55,"license":15,"version":56,"description":7,"dependencies":57},[50,24,51,29,28,52,30,26,53,54],"Data Processing","Distributed Computing","ETL","PyTorch","TensorFlow","luo-kai","1.0.0",[58,59,39],"ray[data]","pyarrow","main","4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c","ai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fray-data",1,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai","https:\u002F\u002Fgithub.com\u002Fluokai0","a solo builder.","2026-05-06 04:29:04",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill ray-data",{"tool":74,"command":75},"bunx","bunx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill ray-data",{"tool":77,"command":78},"pnpm","pnpm dlx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill ray-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fray-data\u002FSKILL.md",true,[82,88],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fintegration.md","REFERENCE","参考文档",1851,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fray-data\u002Freferences\u002Fintegration.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ftransformations.md",1664,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fray-data\u002Freferences\u002Ftransformations.md",[93,120,137,167,199,234],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":44},"01M6F2SKTE208174C7S7JJCZC7","write-contracts","Generates secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. Includes comprehensive storage decision framework for optimal data structure selection. Triggers on: 'write contract', 'create NFT collection', 'build marketplace', 'implement minting', 'generate Move module', 'create token contract', 'build DAO', 'implement staking'. Ask storage questions when: 'store', 'track', 'registry', 'mapping', 'list', 'collection'.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fwrite-contracts\u002FSKILL.md",[],[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119],"write","contracts","write contracts","storage","secure","create","generate","collection","model","decision","ask","smart-contracts","selection","objects","structure","nft","token","aptos","critical","move",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":44},"01M6F2RFXJ6T9MKW0A7N7GM45B","web-search","通用网络搜索技能，支持多引擎搜索（百度、必应、DuckDuckGo），无需API密钥即可获取实时信息","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fweb-search-ex-skill\u002FSKILL.md",[],[127,128,129,130,131,132,133,134,135,136],"web","search","web search","搜索","baidu","bing","duckduckgo","百度","多引擎","internet",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":148,"publishedAt":44},"01M6F2K968G1FZEZFPW2E6B222","solo-build","Execute implementation plan tasks with TDD workflow, auto-commit, and phase gates. Use when user says \"build it\", \"start building\", \"execute plan\", \"implement tasks\", \"ship it\", or references a track ID. Do NOT use for planning (use \u002Fplan) or scaffolding (use \u002Fscaffold).","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fsolo-build\u002FSKILL.md",[143],{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[149,150,151,152,153,154,155,156,128,157,158,159,100,160,161,162,163,164,165,166],"build","solo","solo build","MCP","workflow","commit","tasks","code","Bash","plan","read","auto","references","context7","query","scaffolding","ship it","project",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":179,"publishedAt":44},"01M6F2GKN39SGHSPWYB3662YC8","react-email","Use when building HTML email templates with React components, adding a visual email editor to an application using the React Email visual editor, rendering emails to HTML, or sending emails with Resend. Covers welcome emails, password resets, notifications, order confirmations, newsletters, transactional emails, and the embeddable email editor component.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Freact-email-skills\u002FSKILL.md",[173,176],{"categoryNo":174,"categoryName":175,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":177,"categoryName":178,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000009","前端开发",[180,181,182,183,184,185,186,187,188,189,190,191,192,37,193,194,195,196,197,198],"react","email","react email","github","editor","components","react components","component","welcome","https","notifications","password","node","html","resend","visual","emails","rendering","with",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":215,"publishedAt":44},"01M6F2FBVGVDFSG8SSFJGXA40C","APITester Agent-Driven API Testing","Test API endpoints and document responses. Define tests in plain English, run them, get formatted results. Agent-driven Postman alternative.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fopenclaw-api-tester\u002FSKILL.md",[205,208,211,212],{"categoryNo":206,"categoryName":207,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":209,"categoryName":210,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师",[216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233],"testing","api","api testing","driven","APITester Agent Driven API Testing","test","run","postman","results","plain english","tester","endpoints","document","get","define","tests","english","api-tester",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":240,"publishedAt":44},"01M6F273Y3NGAPVJZ608BC0G9G","hubspot-implementation-plan","Generate a phased implementation plan from a HubSpot audit report. Creates prioritized, sequenced cleanup processes with effort estimates, dependencies, and automation feasibility. Use after running \u002Fhubspot-audit.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fhubspot-implementation-plan\u002FSKILL.md",[],[158,241,242,243,244,245,246,106,247,248,249,250,251,37,252,253,198,254],"implementation","hubspot","hubspot implementation plan","automation","feasibility","cleanup","planning","report","audit","audit report","phased implementation","dependencies","processes","hubspot audit"]