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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: Orchestra Research\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\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\n",{},"main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-ray-data",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-12 18:26:48",{"source":7,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill data-processing-ray-data",{"tool":47,"command":48},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill data-processing-ray-data",{"tool":50,"command":51},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill data-processing-ray-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-ray-data\u002FSKILL.md",true,[55,61],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002Fintegration.md","REFERENCE","参考文档",1852,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-ray-data\u002Freferences\u002Fintegration.md",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"references\u002Ftransformations.md",1665,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-ray-data\u002Freferences\u002Ftransformations.md",[66,100,126,154,181,206],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":71,"sourceHtmlUrl":72,"categories":73,"keywords":79,"publishedAt":29},"01M6R4ARN4W2VZRDERDTMN1KP0","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). 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Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[107,110],{"categoryNo":108,"categoryName":109,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},[112,113,114,85,115,116,81,117,118,119,120,121,122,123,124,125,98],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":130,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":29},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153],"think tank","think","design","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs","when",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":158,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":29},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000023","Skill与智能体开发",[165,166,167,168,169,170,171,172,173,174,175,176,177,178,125,98,179,153,180],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":29},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[188,189,190,191,192,193,194,195,94,196,197,198,199,200,201,202,203,204,205,153],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":210,"sourceHtmlUrl":211,"categories":212,"keywords":219,"publishedAt":29},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[213,216],{"categoryNo":214,"categoryName":215,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":217,"categoryName":218,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000016","图像与多媒体",[220,221,222,166,223,224,225,226,171,94,149,178,151,227,228,98,229,153],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]