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Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-nemo-curator\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},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"processing","data processing nemo curator","NeMo","data","nemo curator","data processing","pii","features","curation","GPU Acceleration","across","text","Audio","Video","dask","image","heuristics","deduplication","multimodal","gpu","training","web","research","2026-11-10 10:12:30",false,"---\nname: data-processing-nemo-curator\ndescription: GPU-accelerated data curation for LLM training. Supports text\u002Fimage\u002Fvideo\u002Faudio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Data Processing, NeMo Curator, Data Curation, GPU Acceleration, Deduplication, Quality Filtering, NVIDIA, RAPIDS, PII Redaction, Multimodal, LLM Training Data]\n  dependencies: [nemo-curator, cudf, dask, rapids]\n---\n\n# NeMo Curator - GPU-Accelerated Data Curation\n\nNVIDIA's toolkit for preparing high-quality training data for LLMs.\n\n## When to use NeMo Curator\n\n**Use NeMo Curator when:**\n- Preparing LLM training data from web scrapes (Common Crawl)\n- Need fast deduplication (16× faster than CPU)\n- Curating multi-modal datasets (text, images, video, audio)\n- Filtering low-quality or toxic content\n- Scaling data processing across GPU cluster\n\n**Performance**:\n- **16× faster** fuzzy deduplication (8TB RedPajama v2)\n- **40% lower TCO** vs CPU alternatives\n- **Near-linear scaling** across GPU nodes\n\n**Use alternatives instead**:\n- **datatrove**: CPU-based, open-source data processing\n- **dolma**: Allen AI's data toolkit\n- **Ray Data**: General ML data processing (no curation focus)\n\n## Quick start\n\n### Installation\n\n```bash\n# Text curation (CUDA 12)\nuv pip install \"nemo-curator[text_cuda12]\"\n\n# All modalities\nuv pip install \"nemo-curator[all_cuda12]\"\n\n# CPU-only (slower)\nuv pip install \"nemo-curator[cpu]\"\n```\n\n### Basic text curation pipeline\n\n```python\nfrom nemo_curator import ScoreFilter, Modify\nfrom nemo_curator.datasets import DocumentDataset\nimport pandas as pd\n\n# Load data\ndf = pd.DataFrame({\"text\": [\"Good document\", \"Bad doc\", \"Excellent text\"]})\ndataset = DocumentDataset(df)\n\n# Quality filtering\ndef quality_score(doc):\n    return len(doc[\"text\"].split()) > 5  # Filter short docs\n\nfiltered = ScoreFilter(quality_score)(dataset)\n\n# Deduplication\nfrom nemo_curator.modules import ExactDuplicates\ndeduped = ExactDuplicates()(filtered)\n\n# Save\ndeduped.to_parquet(\"curated_data\u002F\")\n```\n\n## Data curation pipeline\n\n### Stage 1: Quality filtering\n\n```python\nfrom nemo_curator.filters import (\n    WordCountFilter,\n    RepeatedLinesFilter,\n    UrlRatioFilter,\n    NonAlphaNumericFilter\n)\n\n# Apply 30+ heuristic filters\nfrom nemo_curator import ScoreFilter\n\n# Word count filter\ndataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))\n\n# Remove repetitive content\ndataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))\n\n# URL ratio filter\ndataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))\n```\n\n### Stage 2: Deduplication\n\n**Exact deduplication**:\n```python\nfrom nemo_curator.modules import ExactDuplicates\n\n# Remove exact duplicates\ndeduped = ExactDuplicates(id_field=\"id\", text_field=\"text\")(dataset)\n```\n\n**Fuzzy deduplication** (16× faster on GPU):\n```python\nfrom nemo_curator.modules import FuzzyDuplicates\n\n# MinHash + LSH deduplication\nfuzzy_dedup = FuzzyDuplicates(\n    id_field=\"id\",\n    text_field=\"text\",\n    num_hashes=260,      # MinHash parameters\n    num_buckets=20,\n    hash_method=\"md5\"\n)\n\ndeduped = fuzzy_dedup(dataset)\n```\n\n**Semantic deduplication**:\n```python\nfrom nemo_curator.modules import SemanticDuplicates\n\n# Embedding-based deduplication\nsemantic_dedup = SemanticDuplicates(\n    id_field=\"id\",\n    text_field=\"text\",\n    embedding_model=\"sentence-transformers\u002Fall-MiniLM-L6-v2\",\n    threshold=0.8  # Cosine similarity threshold\n)\n\ndeduped = semantic_dedup(dataset)\n```\n\n### Stage 3: PII redaction\n\n```python\nfrom nemo_curator.modules import Modify\nfrom nemo_curator.modifiers import PIIRedactor\n\n# Redact personally identifiable information\npii_redactor = PIIRedactor(\n    supported_entities=[\"EMAIL_ADDRESS\", \"PHONE_NUMBER\", \"PERSON\", \"LOCATION\"],\n    anonymize_action=\"replace\"  # or \"redact\"\n)\n\nredacted = Modify(pii_redactor)(dataset)\n```\n\n### Stage 4: Classifier filtering\n\n```python\nfrom nemo_curator.classifiers import QualityClassifier\n\n# Quality classification\nquality_clf = QualityClassifier(\n    model_path=\"nvidia\u002Fquality-classifier-deberta\",\n    batch_size=256,\n    device=\"cuda\"\n)\n\n# Filter low-quality documents\nhigh_quality = dataset.filter(lambda doc: quality_clf(doc[\"text\"]) > 0.5)\n```\n\n## GPU acceleration\n\n### GPU vs CPU performance\n\n| Operation | CPU (16 cores) | GPU (A100) | Speedup |\n|-----------|----------------|------------|---------|\n| Fuzzy dedup (8TB) | 120 hours | 7.5 hours | 16× |\n| Exact dedup (1TB) | 8 hours | 0.5 hours | 16× |\n| Quality filtering | 2 hours | 0.2 hours | 10× |\n\n### Multi-GPU scaling\n\n```python\nfrom nemo_curator import get_client\nimport dask_cuda\n\n# Initialize GPU cluster\nclient = get_client(cluster_type=\"gpu\", n_workers=8)\n\n# Process with 8 GPUs\ndeduped = FuzzyDuplicates(...)(dataset)\n```\n\n## Multi-modal curation\n\n### Image curation\n\n```python\nfrom nemo_curator.image import (\n    AestheticFilter,\n    NSFWFilter,\n    CLIPEmbedder\n)\n\n# Aesthetic scoring\naesthetic_filter = AestheticFilter(threshold=5.0)\nfiltered_images = aesthetic_filter(image_dataset)\n\n# NSFW detection\nnsfw_filter = NSFWFilter(threshold=0.9)\nsafe_images = nsfw_filter(filtered_images)\n\n# Generate CLIP embeddings\nclip_embedder = CLIPEmbedder(model=\"openai\u002Fclip-vit-base-patch32\")\nimage_embeddings = clip_embedder(safe_images)\n```\n\n### Video curation\n\n```python\nfrom nemo_curator.video import (\n    SceneDetector,\n    ClipExtractor,\n    InternVideo2Embedder\n)\n\n# Detect scenes\nscene_detector = SceneDetector(threshold=27.0)\nscenes = scene_detector(video_dataset)\n\n# Extract clips\nclip_extractor = ClipExtractor(min_duration=2.0, max_duration=10.0)\nclips = clip_extractor(scenes)\n\n# Generate embeddings\nvideo_embedder = InternVideo2Embedder()\nvideo_embeddings = video_embedder(clips)\n```\n\n### Audio curation\n\n```python\nfrom nemo_curator.audio import (\n    ASRInference,\n    WERFilter,\n    DurationFilter\n)\n\n# ASR transcription\nasr = ASRInference(model=\"nvidia\u002Fstt_en_fastconformer_hybrid_large_pc\")\ntranscribed = asr(audio_dataset)\n\n# Filter by WER (word error rate)\nwer_filter = WERFilter(max_wer=0.3)\nhigh_quality_audio = wer_filter(transcribed)\n\n# Duration filtering\nduration_filter = DurationFilter(min_duration=1.0, max_duration=30.0)\nfiltered_audio = duration_filter(high_quality_audio)\n```\n\n## Common patterns\n\n### Web scrape curation (Common Crawl)\n\n```python\nfrom nemo_curator import ScoreFilter, Modify\nfrom nemo_curator.filters import *\nfrom nemo_curator.modules import *\nfrom nemo_curator.datasets import DocumentDataset\n\n# Load Common Crawl data\ndataset = DocumentDataset.read_parquet(\"common_crawl\u002F*.parquet\")\n\n# Pipeline\npipeline = [\n    # 1. Quality filtering\n    WordCountFilter(min_words=100, max_words=50000),\n    RepeatedLinesFilter(max_repeated_line_fraction=0.2),\n    SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3),\n    UrlRatioFilter(max_url_ratio=0.3),\n\n    # 2. Language filtering\n    LanguageIdentificationFilter(target_languages=[\"en\"]),\n\n    # 3. Deduplication\n    ExactDuplicates(id_field=\"id\", text_field=\"text\"),\n    FuzzyDuplicates(id_field=\"id\", text_field=\"text\", num_hashes=260),\n\n    # 4. PII redaction\n    PIIRedactor(),\n\n    # 5. NSFW filtering\n    NSFWClassifier(threshold=0.8)\n]\n\n# Execute\nfor stage in pipeline:\n    dataset = stage(dataset)\n\n# Save\ndataset.to_parquet(\"curated_common_crawl\u002F\")\n```\n\n### Distributed processing\n\n```python\nfrom nemo_curator import get_client\nfrom dask_cuda import LocalCUDACluster\n\n# Multi-GPU cluster\ncluster = LocalCUDACluster(n_workers=8)\nclient = get_client(cluster=cluster)\n\n# Process large dataset\ndataset = DocumentDataset.read_parquet(\"s3:\u002F\u002Flarge_dataset\u002F*.parquet\")\ndeduped = FuzzyDuplicates(...)(dataset)\n\n# Cleanup\nclient.close()\ncluster.close()\n```\n\n## Performance benchmarks\n\n### Fuzzy deduplication (8TB RedPajama v2)\n\n- **CPU (256 cores)**: 120 hours\n- **GPU (8× A100)**: 7.5 hours\n- **Speedup**: 16×\n\n### Exact deduplication (1TB)\n\n- **CPU (64 cores)**: 8 hours\n- **GPU (4× A100)**: 0.5 hours\n- **Speedup**: 16×\n\n### Quality filtering (100GB)\n\n- **CPU (32 cores)**: 2 hours\n- **GPU (2× A100)**: 0.2 hours\n- **Speedup**: 10×\n\n## Cost comparison\n\n**CPU-based curation** (AWS c5.18xlarge × 10):\n- Cost: $3.60\u002Fhour × 10 = $36\u002Fhour\n- Time for 8TB: 120 hours\n- **Total**: $4,320\n\n**GPU-based curation** (AWS p4d.24xlarge × 2):\n- Cost: $32.77\u002Fhour × 2 = $65.54\u002Fhour\n- Time for 8TB: 7.5 hours\n- **Total**: $491.55\n\n**Savings**: 89% reduction ($3,828 saved)\n\n## Supported data formats\n\n- **Input**: Parquet, JSONL, CSV\n- **Output**: Parquet (recommended), JSONL\n- **WebDataset**: TAR archives for multi-modal\n\n## Use cases\n\n**Production deployments**:\n- NVIDIA used NeMo Curator to prepare Nemotron-4 training data\n- Open-source datasets curated: RedPajama v2, The Pile\n\n## References\n\n- **[Filtering Guide](references\u002Ffiltering.md)** - 30+ quality filters, heuristics\n- **[Deduplication Guide](references\u002Fdeduplication.md)** - Exact, fuzzy, semantic methods\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Curator ⭐ 500+\n- **Docs**: https:\u002F\u002Fdocs.nvidia.com\u002Fnemo-framework\u002Fuser-guide\u002Flatest\u002Fdatacuration\u002F\n- **Version**: 0.4.0+\n- **License**: Apache 2.0\n\n\n\n","# NeMo Curator - GPU-Accelerated Data Curation\n\nNVIDIA's toolkit for preparing high-quality training data for LLMs.\n\n## When to use NeMo Curator\n\n**Use NeMo Curator when:**\n- Preparing LLM training data from web scrapes (Common Crawl)\n- Need fast deduplication (16× faster than CPU)\n- Curating multi-modal datasets (text, images, video, audio)\n- Filtering low-quality or toxic content\n- Scaling data processing across GPU cluster\n\n**Performance**:\n- **16× faster** fuzzy deduplication (8TB RedPajama v2)\n- **40% lower TCO** vs CPU alternatives\n- **Near-linear scaling** across GPU nodes\n\n**Use alternatives instead**:\n- **datatrove**: CPU-based, open-source data processing\n- **dolma**: Allen AI's data toolkit\n- **Ray Data**: General ML data processing (no curation focus)\n\n## Quick start\n\n### Installation\n\n```bash\n# Text curation (CUDA 12)\nuv pip install \"nemo-curator[text_cuda12]\"\n\n# All modalities\nuv pip install \"nemo-curator[all_cuda12]\"\n\n# CPU-only (slower)\nuv pip install \"nemo-curator[cpu]\"\n```\n\n### Basic text curation pipeline\n\n```python\nfrom nemo_curator import ScoreFilter, Modify\nfrom nemo_curator.datasets import DocumentDataset\nimport pandas as pd\n\n# Load data\ndf = pd.DataFrame({\"text\": [\"Good document\", \"Bad doc\", \"Excellent text\"]})\ndataset = DocumentDataset(df)\n\n# Quality filtering\ndef quality_score(doc):\n    return len(doc[\"text\"].split()) > 5  # Filter short docs\n\nfiltered = ScoreFilter(quality_score)(dataset)\n\n# Deduplication\nfrom nemo_curator.modules import ExactDuplicates\ndeduped = ExactDuplicates()(filtered)\n\n# Save\ndeduped.to_parquet(\"curated_data\u002F\")\n```\n\n## Data curation pipeline\n\n### Stage 1: Quality filtering\n\n```python\nfrom nemo_curator.filters import (\n    WordCountFilter,\n    RepeatedLinesFilter,\n    UrlRatioFilter,\n    NonAlphaNumericFilter\n)\n\n# Apply 30+ heuristic filters\nfrom nemo_curator import ScoreFilter\n\n# Word count filter\ndataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))\n\n# Remove repetitive content\ndataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))\n\n# URL ratio filter\ndataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))\n```\n\n### Stage 2: Deduplication\n\n**Exact deduplication**:\n```python\nfrom nemo_curator.modules import ExactDuplicates\n\n# Remove exact duplicates\ndeduped = ExactDuplicates(id_field=\"id\", text_field=\"text\")(dataset)\n```\n\n**Fuzzy deduplication** (16× faster on GPU):\n```python\nfrom nemo_curator.modules import FuzzyDuplicates\n\n# MinHash + LSH deduplication\nfuzzy_dedup = FuzzyDuplicates(\n    id_field=\"id\",\n    text_field=\"text\",\n    num_hashes=260,      # MinHash parameters\n    num_buckets=20,\n    hash_method=\"md5\"\n)\n\ndeduped = fuzzy_dedup(dataset)\n```\n\n**Semantic deduplication**:\n```python\nfrom nemo_curator.modules import SemanticDuplicates\n\n# Embedding-based deduplication\nsemantic_dedup = SemanticDuplicates(\n    id_field=\"id\",\n    text_field=\"text\",\n    embedding_model=\"sentence-transformers\u002Fall-MiniLM-L6-v2\",\n    threshold=0.8  # Cosine similarity threshold\n)\n\ndeduped = semantic_dedup(dataset)\n```\n\n### Stage 3: PII redaction\n\n```python\nfrom nemo_curator.modules import Modify\nfrom nemo_curator.modifiers import PIIRedactor\n\n# Redact personally identifiable information\npii_redactor = PIIRedactor(\n    supported_entities=[\"EMAIL_ADDRESS\", \"PHONE_NUMBER\", \"PERSON\", \"LOCATION\"],\n    anonymize_action=\"replace\"  # or \"redact\"\n)\n\nredacted = Modify(pii_redactor)(dataset)\n```\n\n### Stage 4: Classifier filtering\n\n```python\nfrom nemo_curator.classifiers import QualityClassifier\n\n# Quality classification\nquality_clf = QualityClassifier(\n    model_path=\"nvidia\u002Fquality-classifier-deberta\",\n    batch_size=256,\n    device=\"cuda\"\n)\n\n# Filter low-quality documents\nhigh_quality = dataset.filter(lambda doc: quality_clf(doc[\"text\"]) > 0.5)\n```\n\n## GPU acceleration\n\n### GPU vs CPU performance\n\n| Operation | CPU (16 cores) | GPU (A100) | Speedup |\n|-----------|----------------|------------|---------|\n| Fuzzy dedup (8TB) | 120 hours | 7.5 hours | 16× |\n| Exact dedup (1TB) | 8 hours | 0.5 hours | 16× |\n| Quality filtering | 2 hours | 0.2 hours | 10× |\n\n### Multi-GPU scaling\n\n```python\nfrom nemo_curator import get_client\nimport dask_cuda\n\n# Initialize GPU cluster\nclient = get_client(cluster_type=\"gpu\", n_workers=8)\n\n# Process with 8 GPUs\ndeduped = FuzzyDuplicates(...)(dataset)\n```\n\n## Multi-modal curation\n\n### Image curation\n\n```python\nfrom nemo_curator.image import (\n    AestheticFilter,\n    NSFWFilter,\n    CLIPEmbedder\n)\n\n# Aesthetic scoring\naesthetic_filter = AestheticFilter(threshold=5.0)\nfiltered_images = aesthetic_filter(image_dataset)\n\n# NSFW detection\nnsfw_filter = NSFWFilter(threshold=0.9)\nsafe_images = nsfw_filter(filtered_images)\n\n# Generate CLIP embeddings\nclip_embedder = CLIPEmbedder(model=\"openai\u002Fclip-vit-base-patch32\")\nimage_embeddings = clip_embedder(safe_images)\n```\n\n### Video curation\n\n```python\nfrom nemo_curator.video import (\n    SceneDetector,\n    ClipExtractor,\n    InternVideo2Embedder\n)\n\n# Detect scenes\nscene_detector = SceneDetector(threshold=27.0)\nscenes = scene_detector(video_dataset)\n\n# Extract clips\nclip_extractor = ClipExtractor(min_duration=2.0, max_duration=10.0)\nclips = clip_extractor(scenes)\n\n# Generate embeddings\nvideo_embedder = InternVideo2Embedder()\nvideo_embeddings = video_embedder(clips)\n```\n\n### Audio curation\n\n```python\nfrom nemo_curator.audio import (\n    ASRInference,\n    WERFilter,\n    DurationFilter\n)\n\n# ASR transcription\nasr = ASRInference(model=\"nvidia\u002Fstt_en_fastconformer_hybrid_large_pc\")\ntranscribed = asr(audio_dataset)\n\n# Filter by WER (word error rate)\nwer_filter = WERFilter(max_wer=0.3)\nhigh_quality_audio = wer_filter(transcribed)\n\n# Duration filtering\nduration_filter = DurationFilter(min_duration=1.0, max_duration=30.0)\nfiltered_audio = duration_filter(high_quality_audio)\n```\n\n## Common patterns\n\n### Web scrape curation (Common Crawl)\n\n```python\nfrom nemo_curator import ScoreFilter, Modify\nfrom nemo_curator.filters import *\nfrom nemo_curator.modules import *\nfrom nemo_curator.datasets import DocumentDataset\n\n# Load Common Crawl data\ndataset = DocumentDataset.read_parquet(\"common_crawl\u002F*.parquet\")\n\n# Pipeline\npipeline = [\n    # 1. Quality filtering\n    WordCountFilter(min_words=100, max_words=50000),\n    RepeatedLinesFilter(max_repeated_line_fraction=0.2),\n    SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3),\n    UrlRatioFilter(max_url_ratio=0.3),\n\n    # 2. Language filtering\n    LanguageIdentificationFilter(target_languages=[\"en\"]),\n\n    # 3. Deduplication\n    ExactDuplicates(id_field=\"id\", text_field=\"text\"),\n    FuzzyDuplicates(id_field=\"id\", text_field=\"text\", num_hashes=260),\n\n    # 4. PII redaction\n    PIIRedactor(),\n\n    # 5. NSFW filtering\n    NSFWClassifier(threshold=0.8)\n]\n\n# Execute\nfor stage in pipeline:\n    dataset = stage(dataset)\n\n# Save\ndataset.to_parquet(\"curated_common_crawl\u002F\")\n```\n\n### Distributed processing\n\n```python\nfrom nemo_curator import get_client\nfrom dask_cuda import LocalCUDACluster\n\n# Multi-GPU cluster\ncluster = LocalCUDACluster(n_workers=8)\nclient = get_client(cluster=cluster)\n\n# Process large dataset\ndataset = DocumentDataset.read_parquet(\"s3:\u002F\u002Flarge_dataset\u002F*.parquet\")\ndeduped = FuzzyDuplicates(...)(dataset)\n\n# Cleanup\nclient.close()\ncluster.close()\n```\n\n## Performance benchmarks\n\n### Fuzzy deduplication (8TB RedPajama v2)\n\n- **CPU (256 cores)**: 120 hours\n- **GPU (8× A100)**: 7.5 hours\n- **Speedup**: 16×\n\n### Exact deduplication (1TB)\n\n- **CPU (64 cores)**: 8 hours\n- **GPU (4× A100)**: 0.5 hours\n- **Speedup**: 16×\n\n### Quality filtering (100GB)\n\n- **CPU (32 cores)**: 2 hours\n- **GPU (2× A100)**: 0.2 hours\n- **Speedup**: 10×\n\n## Cost comparison\n\n**CPU-based curation** (AWS c5.18xlarge × 10):\n- Cost: $3.60\u002Fhour × 10 = $36\u002Fhour\n- Time for 8TB: 120 hours\n- **Total**: $4,320\n\n**GPU-based curation** (AWS p4d.24xlarge × 2):\n- Cost: $32.77\u002Fhour × 2 = $65.54\u002Fhour\n- Time for 8TB: 7.5 hours\n- **Total**: $491.55\n\n**Savings**: 89% reduction ($3,828 saved)\n\n## Supported data formats\n\n- **Input**: Parquet, JSONL, CSV\n- **Output**: Parquet (recommended), JSONL\n- **WebDataset**: TAR archives for multi-modal\n\n## Use cases\n\n**Production deployments**:\n- NVIDIA used NeMo Curator to prepare Nemotron-4 training data\n- Open-source datasets curated: RedPajama v2, The Pile\n\n## References\n\n- **[Filtering Guide](references\u002Ffiltering.md)** - 30+ quality filters, heuristics\n- **[Deduplication Guide](references\u002Fdeduplication.md)** - Exact, fuzzy, semantic methods\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Curator ⭐ 500+\n- **Docs**: https:\u002F\u002Fdocs.nvidia.com\u002Fnemo-framework\u002Fuser-guide\u002Flatest\u002Fdatacuration\u002F\n- **Version**: 0.4.0+\n- **License**: Apache 2.0\n\n\n\n",{"name":5,"license":15,"metadata":57,"description":7},{"tags":58,"author":69,"version":70,"dependencies":71},[59,60,61,38,62,63,64,65,66,67,68],"Data Processing","NeMo Curator","Data Curation","Deduplication","Quality Filtering","NVIDIA","RAPIDS","PII Redaction","Multimodal","LLM Training Data","Orchestra Research","1.0.0",[72,73,43,74],"nemo-curator","cudf","rapids","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-nemo-curator",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":86,"prompt":96,"zipAvailable":97},[87,90,93],{"tool":88,"command":89},"npx","npx skills add davila7\u002Fclaude-code-templates --skill data-processing-nemo-curator",{"tool":91,"command":92},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill data-processing-nemo-curator",{"tool":94,"command":95},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill data-processing-nemo-curator","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-nemo-curator\u002FSKILL.md",true,[99,105,109,113,117,123],{"path":100,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fdeduplication.md","REFERENCE","参考文档",2142,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-nemo-curator\u002Freferences\u002Fdeduplication.md",{"path":106,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Ffiltering.md",2351,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdata-processing-nemo-curator\u002Freferences\u002Ffiltering.md",{"path":110,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":111,"sourceHtmlUrl":112},"sections\u002Fmanifest.json",676,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fgstack\u002Fdesign-consultation\u002Fsections\u002Fmanifest.json",{"path":114,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":115,"sourceHtmlUrl":116},"sections\u002Fproposal-and-preview.md",21353,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fgstack\u002Fdesign-consultation\u002Fsections\u002Fproposal-and-preview.md",{"path":118,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":121,"sourceHtmlUrl":122},"sections\u002Fproposal-and-preview.md.tmpl","OTHER","其他",16422,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fgstack\u002Fdesign-consultation\u002Fsections\u002Fproposal-and-preview.md.tmpl",{"path":124,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":125,"sourceHtmlUrl":126},"SKILL.md.tmpl",9554,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fgstack\u002Fdesign-consultation\u002FSKILL.md.tmpl",[128,158,185,214,248,272],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":137,"publishedAt":52},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[134],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":165,"publishedAt":52},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[164],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},[166,167,168,169,170,171,172,173,174,175,176,147,177,178,179,180,181,182,183,184],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":196,"publishedAt":52},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[191,194,195],{"categoryNo":192,"categoryName":193,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[197,198,199,200,201,202,203,143,204,205,206,207,208,209,210,146,211,212,147,213],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":218,"licenseFileUrl":219,"sourceHtmlUrl":220,"categories":221,"keywords":225,"publishedAt":52},"01M6AFY6F7CC2BBZP1QD6VTNVC","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). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[222],{"categoryNo":223,"categoryName":224,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[226,50,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,143,247],"artifacts builder","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":249,"name":250,"nameZh":6,"description":251,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":218,"licenseFileUrl":252,"sourceHtmlUrl":253,"categories":254,"keywords":259,"publishedAt":52},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[255,258],{"categoryNo":256,"categoryName":257,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":223,"categoryName":224,"taxonomyType":21,"taxonomyTypeDesc":22},[260,261,262,263,264,50,265,235,236,266,267,268,269,143,270,149,271],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":273,"name":274,"nameZh":6,"description":275,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":218,"licenseFileUrl":276,"sourceHtmlUrl":277,"categories":278,"keywords":286,"publishedAt":52},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[279,282,283],{"categoryNo":280,"categoryName":281,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":284,"categoryName":285,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[287,288,289,290,291,292,32,293,228,294,238,177,295,296,297,298,299,209,300,147,301,302,303,304,305,155,306],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]