[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FCE3G8126RVB13CTHVDCZT":3,"skill-same:01M6FCE3G8126RVB13CTHVDCZT":87},{"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":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6FCE3G8126RVB13CTHVDCZT","sentencepiece",null,"Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1992,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"cjk","when","orchestra","you","Unicode","CJK Languages","Language-Independent","Unigram","BPE","Tokenization","google","as","algorithms","deterministic","multilingual","use","text","Transformers","Reproducible","support","memory","2026-11-10 10:12:30",false,"---\nname: sentencepiece\ndescription: Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Tokenization, SentencePiece, Language-Independent, BPE, Unigram, Multilingual, CJK Languages, Unicode, Deterministic, Google]\ndependencies: [sentencepiece, transformers]\n---\n\n# SentencePiece - Language-Independent Tokenization\n\nUnsupervised tokenizer that works on raw text without language-specific preprocessing.\n\n## When to use SentencePiece\n\n**Use SentencePiece when:**\n- Building multilingual models (no language-specific rules)\n- Working with CJK languages (Chinese, Japanese, Korean)\n- Need reproducible tokenization (deterministic vocabulary)\n- Want to train on raw text (no pre-tokenization needed)\n- Require lightweight deployment (6MB memory, 50k sentences\u002Fsec)\n\n**Performance**:\n- **Speed**: 50,000 sentences\u002Fsec\n- **Memory**: ~6MB for loaded model\n- **Languages**: All (language-independent)\n\n**Use alternatives instead**:\n- **HuggingFace Tokenizers**: Faster training, more flexibility\n- **tiktoken**: OpenAI models (GPT-3.5\u002F4)\n- **BERT WordPiece**: English-centric tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install sentencepiece\n\n# C++ (requires CMake)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece.git\ncd sentencepiece\nmkdir build && cd build\ncmake .. && make -j $(nproc)\nsudo make install\n```\n\n### Train model\n\n```bash\n# Command-line (BPE with 8000 vocab)\nspm_train --input=data.txt --model_prefix=m --vocab_size=8000 --model_type=bpe\n\n# Python API\nimport sentencepiece as spm\n\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='m',\n    vocab_size=8000,\n    model_type='bpe'\n)\n```\n\n**Training time**: ~1-2 minutes for 100MB corpus\n\n### Encode and decode\n\n```python\nimport sentencepiece as spm\n\n# Load model\nsp = spm.SentencePieceProcessor(model_file='m.model')\n\n# Encode to pieces\npieces = sp.encode('This is a test', out_type=str)\nprint(pieces)  # ['▁This', '▁is', '▁a', '▁test']\n\n# Encode to IDs\nids = sp.encode('This is a test', out_type=int)\nprint(ids)  # [284, 47, 11, 1243]\n\n# Decode\ntext = sp.decode(ids)\nprint(text)  # \"This is a test\"\n```\n\n## Language-independent design\n\n### Whitespace as symbol (▁)\n\n```python\ntext = \"Hello world\"\npieces = sp.encode(text, out_type=str)\nprint(pieces)  # ['▁Hello', '▁world']\n\n# Decode preserves spaces\ndecoded = sp.decode_pieces(pieces)\nprint(decoded)  # \"Hello world\"\n```\n\n**Key principle**: Treat text as raw Unicode, whitespace = ▁ (meta symbol)\n\n## Tokenization algorithms\n\n### BPE (Byte-Pair Encoding)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='bpe_model',\n    vocab_size=16000,\n    model_type='bpe'\n)\n```\n\n**Used by**: mBART\n\n### Unigram (default)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='unigram_model',\n    vocab_size=8000,\n    model_type='unigram'\n)\n```\n\n**Used by**: T5, ALBERT, XLNet\n\n## Training configuration\n\n### Essential parameters\n\n```python\nspm.SentencePieceTrainer.train(\n    input='corpus.txt',\n    model_prefix='m',\n    vocab_size=32000,\n    model_type='unigram',\n    character_coverage=0.9995,  # 1.0 for CJK\n    user_defined_symbols=['[SEP]', '[CLS]'],\n    unk_piece='\u003Cunk>',\n    num_threads=16\n)\n```\n\n### Character coverage\n\n| Language Type | Coverage | Rationale |\n|---------------|----------|-----------|\n| English       | 0.9995   | Most common chars |\n| CJK (Chinese) | 1.0      | All characters needed |\n| Multilingual  | 0.9995   | Balance |\n\n## Encoding options\n\n### Subword regularization\n\n```python\n# Sample different tokenizations\nfor _ in range(3):\n    pieces = sp.encode('tokenization', out_type=str, enable_sampling=True, alpha=0.1)\n    print(pieces)\n\n# Output (different each time):\n# ['▁token', 'ization']\n# ['▁tok', 'en', 'ization']\n```\n\n**Use case**: Data augmentation for robustness.\n\n## Common patterns\n\n### T5-style training\n\n```python\nspm.SentencePieceTrainer.train(\n    input='c4_corpus.txt',\n    model_prefix='t5',\n    vocab_size=32000,\n    model_type='unigram',\n    user_defined_symbols=[f'\u003Cextra_id_{i}>' for i in range(100)],\n    unk_id=2,\n    eos_id=1,\n    pad_id=0\n)\n```\n\n### Integration with transformers\n\n```python\nfrom transformers import T5Tokenizer\n\n# T5 uses SentencePiece internally\ntokenizer = T5Tokenizer.from_pretrained('t5-base')\ninputs = tokenizer('translate English to French: Hello', return_tensors='pt')\n```\n\n## Performance benchmarks\n\n### Training speed\n\n| Corpus | BPE (16k) | Unigram (8k) |\n|--------|-----------|--------------|\n| 100 MB | 1-2 min   | 3-4 min      |\n| 1 GB   | 10-15 min | 30-40 min    |\n\n### Tokenization speed\n\n- **SentencePiece**: 50,000 sentences\u002Fsec\n- **HF Tokenizers**: 200,000 sentences\u002Fsec (4× faster)\n\n## Supported models\n\n**T5 family**: `t5-base`, `t5-large` (32k vocab, Unigram)\n**ALBERT**: `albert-base-v2` (30k vocab, Unigram)\n**XLNet**: `xlnet-base-cased` (32k vocab, Unigram)\n**mBART**: `facebook\u002Fmbart-large-50` (250k vocab, BPE)\n\n## References\n\n- **[Training Guide](references\u002Ftraining.md)** - Detailed options, corpus preparation\n- **[Algorithms](references\u002Falgorithms.md)** - BPE vs Unigram, subword regularization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece ⭐ 10,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06226 (EMNLP 2018)\n- **Version**: 0.2.0+\n\n\n","# SentencePiece - Language-Independent Tokenization\n\nUnsupervised tokenizer that works on raw text without language-specific preprocessing.\n\n## When to use SentencePiece\n\n**Use SentencePiece when:**\n- Building multilingual models (no language-specific rules)\n- Working with CJK languages (Chinese, Japanese, Korean)\n- Need reproducible tokenization (deterministic vocabulary)\n- Want to train on raw text (no pre-tokenization needed)\n- Require lightweight deployment (6MB memory, 50k sentences\u002Fsec)\n\n**Performance**:\n- **Speed**: 50,000 sentences\u002Fsec\n- **Memory**: ~6MB for loaded model\n- **Languages**: All (language-independent)\n\n**Use alternatives instead**:\n- **HuggingFace Tokenizers**: Faster training, more flexibility\n- **tiktoken**: OpenAI models (GPT-3.5\u002F4)\n- **BERT WordPiece**: English-centric tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install sentencepiece\n\n# C++ (requires CMake)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece.git\ncd sentencepiece\nmkdir build && cd build\ncmake .. && make -j $(nproc)\nsudo make install\n```\n\n### Train model\n\n```bash\n# Command-line (BPE with 8000 vocab)\nspm_train --input=data.txt --model_prefix=m --vocab_size=8000 --model_type=bpe\n\n# Python API\nimport sentencepiece as spm\n\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='m',\n    vocab_size=8000,\n    model_type='bpe'\n)\n```\n\n**Training time**: ~1-2 minutes for 100MB corpus\n\n### Encode and decode\n\n```python\nimport sentencepiece as spm\n\n# Load model\nsp = spm.SentencePieceProcessor(model_file='m.model')\n\n# Encode to pieces\npieces = sp.encode('This is a test', out_type=str)\nprint(pieces)  # ['▁This', '▁is', '▁a', '▁test']\n\n# Encode to IDs\nids = sp.encode('This is a test', out_type=int)\nprint(ids)  # [284, 47, 11, 1243]\n\n# Decode\ntext = sp.decode(ids)\nprint(text)  # \"This is a test\"\n```\n\n## Language-independent design\n\n### Whitespace as symbol (▁)\n\n```python\ntext = \"Hello world\"\npieces = sp.encode(text, out_type=str)\nprint(pieces)  # ['▁Hello', '▁world']\n\n# Decode preserves spaces\ndecoded = sp.decode_pieces(pieces)\nprint(decoded)  # \"Hello world\"\n```\n\n**Key principle**: Treat text as raw Unicode, whitespace = ▁ (meta symbol)\n\n## Tokenization algorithms\n\n### BPE (Byte-Pair Encoding)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='bpe_model',\n    vocab_size=16000,\n    model_type='bpe'\n)\n```\n\n**Used by**: mBART\n\n### Unigram (default)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='unigram_model',\n    vocab_size=8000,\n    model_type='unigram'\n)\n```\n\n**Used by**: T5, ALBERT, XLNet\n\n## Training configuration\n\n### Essential parameters\n\n```python\nspm.SentencePieceTrainer.train(\n    input='corpus.txt',\n    model_prefix='m',\n    vocab_size=32000,\n    model_type='unigram',\n    character_coverage=0.9995,  # 1.0 for CJK\n    user_defined_symbols=['[SEP]', '[CLS]'],\n    unk_piece='\u003Cunk>',\n    num_threads=16\n)\n```\n\n### Character coverage\n\n| Language Type | Coverage | Rationale |\n|---------------|----------|-----------|\n| English       | 0.9995   | Most common chars |\n| CJK (Chinese) | 1.0      | All characters needed |\n| Multilingual  | 0.9995   | Balance |\n\n## Encoding options\n\n### Subword regularization\n\n```python\n# Sample different tokenizations\nfor _ in range(3):\n    pieces = sp.encode('tokenization', out_type=str, enable_sampling=True, alpha=0.1)\n    print(pieces)\n\n# Output (different each time):\n# ['▁token', 'ization']\n# ['▁tok', 'en', 'ization']\n```\n\n**Use case**: Data augmentation for robustness.\n\n## Common patterns\n\n### T5-style training\n\n```python\nspm.SentencePieceTrainer.train(\n    input='c4_corpus.txt',\n    model_prefix='t5',\n    vocab_size=32000,\n    model_type='unigram',\n    user_defined_symbols=[f'\u003Cextra_id_{i}>' for i in range(100)],\n    unk_id=2,\n    eos_id=1,\n    pad_id=0\n)\n```\n\n### Integration with transformers\n\n```python\nfrom transformers import T5Tokenizer\n\n# T5 uses SentencePiece internally\ntokenizer = T5Tokenizer.from_pretrained('t5-base')\ninputs = tokenizer('translate English to French: Hello', return_tensors='pt')\n```\n\n## Performance benchmarks\n\n### Training speed\n\n| Corpus | BPE (16k) | Unigram (8k) |\n|--------|-----------|--------------|\n| 100 MB | 1-2 min   | 3-4 min      |\n| 1 GB   | 10-15 min | 30-40 min    |\n\n### Tokenization speed\n\n- **SentencePiece**: 50,000 sentences\u002Fsec\n- **HF Tokenizers**: 200,000 sentences\u002Fsec (4× faster)\n\n## Supported models\n\n**T5 family**: `t5-base`, `t5-large` (32k vocab, Unigram)\n**ALBERT**: `albert-base-v2` (30k vocab, Unigram)\n**XLNet**: `xlnet-base-cased` (32k vocab, Unigram)\n**mBART**: `facebook\u002Fmbart-large-50` (250k vocab, BPE)\n\n## References\n\n- **[Training Guide](references\u002Ftraining.md)** - Detailed options, corpus preparation\n- **[Algorithms](references\u002Falgorithms.md)** - BPE vs Unigram, subword regularization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece ⭐ 10,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06226 (EMNLP 2018)\n- **Version**: 0.2.0+\n\n\n",{"name":5,"tags":45,"author":50,"license":15,"version":51,"description":7,"dependencies":52},[28,46,25,27,26,47,24,23,48,49],"SentencePiece","Multilingual","Deterministic","Google","Orchestra Research","1.0.0",[5,53],"transformers","main","90b50ed0778745a80fd5bfb13b1843d45b9e82f7","tests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece",233,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-15 01:54:15",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill sentencepiece",{"tool":69,"command":70},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill sentencepiece",{"tool":72,"command":73},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill sentencepiece","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Falgorithms.md","REFERENCE","参考文档",4212,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Falgorithms.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Ftraining.md",6240,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Ftraining.md",[88,128,153,179,206,227],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":93,"sourceHtmlUrl":94,"categories":95,"keywords":101,"publishedAt":40},"01M6FEGPPK0ZTKCCM0R3E3KS33","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\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,34,120,121,122,123,124,125,126,127],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","component","routing","state management","state","shadcn","file","ui","Tools","Tailwind","html","single-file","simple","ui components","multi","for","jsx","suite",{"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":92,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":139,"publishedAt":40},"01M6FEE9VTK1W4GY21AKREJXRG","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\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[135,138],{"categoryNo":136,"categoryName":137,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},[140,141,142,107,143,144,103,145,146,147,117,148,149,150,151,152,125],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","behavior","local","with",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":157,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":40},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[161,162,163,164,165,166,167,168,34,169,170,171,172,173,174,175,152,176,177,178,125,20],"analysis","neuropixels analysis","spike","metrics","data","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":183,"sourceHtmlUrl":184,"categories":185,"keywords":189,"publishedAt":40},"01M6FDP0PDK6F1GGX395C7625Z","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\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[186],{"categoryNo":187,"categoryName":188,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000023","Skill与智能体开发",[190,191,192,193,194,195,196,197,198,199,200,201,202,203,152,125,204,20,205],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":210,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":40},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[213],{"categoryNo":187,"categoryName":188,"taxonomyType":99,"taxonomyTypeDesc":100},[208,29,215,216,217,218,219,147,220,221,34,222,223,224,225,125,226],"browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":231,"sourceHtmlUrl":232,"categories":233,"keywords":240,"publishedAt":40},"01M6FDHPXE10J3XGTCH5HBHS1M","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\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[234,237],{"categoryNo":235,"categoryName":236,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":238,"categoryName":239,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000016","图像与多媒体",[241,242,243,191,244,118,245,246,196,34,247,203,248,249,250,125,251,20],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]