[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG630KEY37P1PKK2CEFSR7":3,"skill-same:01M6XG630KEY37P1PKK2CEFSR7":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":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6XG630KEY37P1PKK2CEFSR7","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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"synthetic","when","you","Unicode","CJK Languages","Language-Independent","Unigram","BPE","Tokenization","google","cjk","algorithms","deterministic","multilingual","text","Tools","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.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\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":43,"author":48,"license":15,"version":49,"category":50,"description":7,"dependencies":51},[27,44,24,26,25,45,23,22,46,47],"SentencePiece","Multilingual","Deterministic","Google","Synthetic Sciences","1.0.0","llm-tools",[5,52],"transformers","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add fadrienne\u002FMyGitHub --skill sentencepiece",{"tool":65,"command":66},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill sentencepiece",{"tool":68,"command":69},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill sentencepiece","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002FSKILL.md",true,[73,79],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Falgorithms.md","REFERENCE","参考文档",4212,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002Freferences\u002Falgorithms.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Ftraining.md",6240,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002Freferences\u002Ftraining.md",[84,115,141,175,202,228],{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":38},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[96,97,98,99,100,101,102,103,20,104,105,106,107,108,109,110,111,112,113,114],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":124,"publishedAt":38},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000008","测试与质量",[106,125,96,126,127,128,20,21,129,130,131,132,133,134,135,136,137,138,139,140],"statistical analysis","academic research","implementing","selection","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":156,"publishedAt":38},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[147,150,151],{"categoryNo":148,"categoryName":149,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[157,158,159,160,161,162,163,164,165,166,104,167,168,169,170,171,172,173,129,174],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":185,"publishedAt":38},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[181,184],{"categoryNo":182,"categoryName":183,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[186,158,187,188,189,190,191,192,193,165,20,194,195,196,197,198,199,200,131,201],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":214,"publishedAt":38},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[208,211],{"categoryNo":209,"categoryName":210,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":212,"categoryName":213,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000030","开发工程师",[215,216,136,217,37,218,219,103,220,20,104,106,221,222,223,224,225,226,173,227],"code","research code","which","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":232,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":241,"publishedAt":38},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[235,238,239,240],{"categoryNo":236,"categoryName":237,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":148,"categoryName":149,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},[157,242,243,244,245,246,186,247,248,20,104,106,249,250,33,251,252,253,158,173],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","documentation","references","diagram"]