[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QYQVMZV12WR6929VTEXM06":3,"skill-same:01M6QYQVMZV12WR6929VTEXM06":83},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6QYQVMZV12WR6929VTEXM06","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.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\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],"Tokenization","when","orchestra","you","Unicode","CJK Languages","Language-Independent","Unigram","BPE","google","cjk","as","algorithms","deterministic","multilingual","text","Transformers","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\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\n",{"name":5,"tags":43,"author":48,"license":15,"version":49,"description":7,"dependencies":50},[19,44,25,27,26,45,24,23,46,47],"SentencePiece","Multilingual","Deterministic","Google","Orchestra Research","1.0.0",[5,51],"transformers","main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-13 18:15:57",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill sentencepiece",{"tool":65,"command":66},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill sentencepiece",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill sentencepiece","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002FSKILL.md",true,[73,79],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Falgorithms.md","REFERENCE","参考文档",4213,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Falgorithms.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Ftraining.md",6241,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Ftraining.md",[84,118,144,171,198,223],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":88,"licenseFileUrl":89,"sourceHtmlUrl":90,"categories":91,"keywords":97,"publishedAt":38},"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). 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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","use","single-file","ui components","multi","for","single",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":88,"licenseFileUrl":122,"sourceHtmlUrl":123,"categories":124,"keywords":129,"publishedAt":38},"01M6R45GXREM5DP8T08MAC788H","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\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",[125,128],{"categoryNo":126,"categoryName":127,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},[130,131,132,103,133,134,99,135,136,137,138,139,140,141,142,143,116],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":148,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":38},"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",[],[152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,20],"think tank","think","design","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":88,"licenseFileUrl":175,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":38},"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",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000023","Skill与智能体开发",[182,183,184,185,186,187,188,189,190,191,192,193,194,195,143,116,196,20,197],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":38},"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",[],[205,206,207,208,209,210,211,212,112,213,214,215,216,217,218,219,220,221,222,20],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":88,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":236,"publishedAt":38},"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",[230,233],{"categoryNo":231,"categoryName":232,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":234,"categoryName":235,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000016","图像与多媒体",[237,238,239,183,240,241,242,243,188,112,167,195,169,244,245,116,246,20],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]