[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M74ZJDW63HM655K5MH83D027":3,"skill-same:01M74ZJDW63HM655K5MH83D027":60},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":35,"ownerHtmlUrl":36,"ownerBlogUrl":37,"ownerDescription":38,"updatedAt":39,"install":40,"resources":53},"01M74ZJDW63HM655K5MH83D027","sentence-transformers",null,"Generates sentence, text, and image embeddings locally with the Python sentence-transformers (SBERT) library, using pre-trained Hugging Face models such as all-MiniLM-L6-v2, all-mpnet-base-v2, and multilingual variants. Covers encoding, cosine similarity, semantic search, batch encoding, fine-tuning, and LangChain\u002FLlamaIndex integration. Use when building embeddings for RAG, running semantic search or similarity scoring, clustering or classifying text, embedding multilingual text without an API, or fine-tuning an embedding model on domain data. For API-based or managed embeddings, use OpenAI or Cohere Embed instead.","KalarisLabs\u002Fresearch-agent-skills","KalarisLabs","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F300878568?v=4","01M74Y7MT23GXXC56VM6TVSXZY","Kalarislabs","01M74Y7MTQM9REXZX4SNXWX95C",132,"MIT","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fsentence-transformers\u002FSKILL.md",[],[],"2026-11-11 17:47:46",false,"---\nname: sentence-transformers\ndescription: Generates sentence, text, and image embeddings locally with the Python sentence-transformers (SBERT) library, using pre-trained Hugging Face models such as all-MiniLM-L6-v2, all-mpnet-base-v2, and multilingual variants. Covers encoding, cosine similarity, semantic search, batch encoding, fine-tuning, and LangChain\u002FLlamaIndex integration. Use when building embeddings for RAG, running semantic search or similarity scoring, clustering or classifying text, embedding multilingual text without an API, or fine-tuning an embedding model on domain data. For API-based or managed embeddings, use OpenAI or Cohere Embed instead.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: knowledge-and-rag\n  maintainer: Kalaris Labs\n  tags: Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production\n  dependencies: sentence-transformers, transformers, torch\n---\n\n# Sentence Transformers - State-of-the-Art Embeddings\n\nPython framework for sentence and text embeddings using transformers.\n\n## When to use Sentence Transformers\n\n**Use when:**\n- Need high-quality embeddings for RAG\n- Semantic similarity and search\n- Text clustering and classification\n- Multilingual embeddings (100+ languages)\n- Running embeddings locally (no API)\n- Cost-effective alternative to OpenAI embeddings\n\n**Metrics**:\n- **5000+ pre-trained models**\n- **100+ languages** supported\n- Based on PyTorch\u002FTransformers\n\n**Use alternatives instead**:\n- **OpenAI Embeddings**: Need API-based, highest quality\n- **Instructor**: Task-specific instructions\n- **Cohere Embed**: Managed service\n\n## Quick start\n\n### Installation\n\n```bash\npip install sentence-transformers\n```\n\n### Basic usage\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Load model\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Generate embeddings\nsentences = [\n    \"This is an example sentence\",\n    \"Each sentence is converted to a vector\"\n]\n\nembeddings = model.encode(sentences)\nprint(embeddings.shape)  # (2, 384)\n\n# Cosine similarity\nfrom sentence_transformers.util import cos_sim\nsimilarity = cos_sim(embeddings[0], embeddings[1])\nprint(f\"Similarity: {similarity.item():.4f}\")\n```\n\n## Popular models\n\n### General purpose\n\n```python\n# Fast, good quality (384 dim)\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Better quality (768 dim)\nmodel = SentenceTransformer('all-mpnet-base-v2')\n\n# Best quality (1024 dim, slower)\nmodel = SentenceTransformer('all-roberta-large-v1')\n```\n\n### Multilingual\n\n```python\n# 50+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\n\n# 100+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')\n```\n\n### Domain-specific\n\n```python\n# Legal domain\nmodel = SentenceTransformer('nlpaueb\u002Flegal-bert-base-uncased')\n\n# Scientific papers\nmodel = SentenceTransformer('allenai\u002Fspecter')\n\n# Code\nmodel = SentenceTransformer('microsoft\u002Fcodebert-base')\n```\n\n## Semantic search\n\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Corpus\ncorpus = [\n    \"Python is a programming language\",\n    \"Machine learning uses algorithms\",\n    \"Neural networks are powerful\"\n]\n\n# Encode corpus\ncorpus_embeddings = model.encode(corpus, convert_to_tensor=True)\n\n# Query\nquery = \"What is Python?\"\nquery_embedding = model.encode(query, convert_to_tensor=True)\n\n# Find most similar\nhits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)\nprint(hits)\n```\n\n## Similarity computation\n\n```python\n# Cosine similarity\nsimilarity = util.cos_sim(embedding1, embedding2)\n\n# Dot product\nsimilarity = util.dot_score(embedding1, embedding2)\n\n# Pairwise cosine similarity\nsimilarities = util.cos_sim(embeddings, embeddings)\n```\n\n## Batch encoding\n\n```python\n# Efficient batch processing\nsentences = [\"sentence 1\", \"sentence 2\", ...] * 1000\n\nembeddings = model.encode(\n    sentences,\n    batch_size=32,\n    show_progress_bar=True,\n    convert_to_tensor=False  # or True for PyTorch tensors\n)\n```\n\n## Fine-tuning\n\n```python\nfrom sentence_transformers import InputExample, losses\nfrom torch.utils.data import DataLoader\n\n# Training data\ntrain_examples = [\n    InputExample(texts=['sentence 1', 'sentence 2'], label=0.8),\n    InputExample(texts=['sentence 3', 'sentence 4'], label=0.3),\n]\n\ntrain_dataloader = DataLoader(train_examples, batch_size=16)\n\n# Loss function\ntrain_loss = losses.CosineSimilarityLoss(model)\n\n# Train\nmodel.fit(\n    train_objectives=[(train_dataloader, train_loss)],\n    epochs=10,\n    warmup_steps=100\n)\n\n# Save\nmodel.save('my-finetuned-model')\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\n# Use with vector stores\nfrom langchain_chroma import Chroma\n\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=embeddings\n)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\n\nembed_model = HuggingFaceEmbedding(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\nfrom llama_index.core import Settings\nSettings.embed_model = embed_model\n\n# Use in index\nindex = VectorStoreIndex.from_documents(documents)\n```\n\n## Model selection guide\n\n| Model | Dimensions | Speed | Quality | Use Case |\n|-------|------------|-------|---------|----------|\n| all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping |\n| all-mpnet-base-v2 | 768 | Medium | Better | Production RAG |\n| all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed |\n| paraphrase-multilingual | 768 | Medium | Good | Multilingual |\n\n## Best practices\n\n1. **Start with all-MiniLM-L6-v2** - Good baseline\n2. **Normalize embeddings** - Better for cosine similarity\n3. **Use GPU if available** - 10× faster encoding\n4. **Batch encoding** - More efficient\n5. **Cache embeddings** - Expensive to recompute\n6. **Fine-tune for domain** - Improves quality\n7. **Test different models** - Quality varies by task\n8. **Monitor memory** - Large models need more RAM\n\n## Performance\n\n| Model | Speed (sentences\u002Fsec) | Memory | Dimension |\n|-------|----------------------|---------|-----------|\n| MiniLM | ~2000 | 120MB | 384 |\n| MPNet | ~600 | 420MB | 768 |\n| RoBERTa | ~300 | 1.3GB | 1024 |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers ⭐ 15,700+\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fsentence-transformers\n- **Docs**: https:\u002F\u002Fwww.sbert.net\n- **License**: Apache 2.0\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the corpus, embedding model and vector store versions, and where the index will live.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Index a small subset and test retrieval on known question-passage pairs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure recall@k on a labeled set; answers cite retrieved passages only.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Retrieval returns irrelevant chunks | Revisit chunking, add BM25\u002Fhybrid search, or re-rank before changing the generator. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Answers must come from retrieved text; say when the corpus does not contain the answer.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `chroma`: Open-source embedding database for AI applications.\n- `pinecone`: Guides use of Pinecone, a managed serverless vector database, through its Python client and the LangChain and LlamaIndex integrations.\n- `qdrant-vector-search`: High-performance vector similarity search engine for RAG and semantic search.\n","# Sentence Transformers - State-of-the-Art Embeddings\n\nPython framework for sentence and text embeddings using transformers.\n\n## When to use Sentence Transformers\n\n**Use when:**\n- Need high-quality embeddings for RAG\n- Semantic similarity and search\n- Text clustering and classification\n- Multilingual embeddings (100+ languages)\n- Running embeddings locally (no API)\n- Cost-effective alternative to OpenAI embeddings\n\n**Metrics**:\n- **5000+ pre-trained models**\n- **100+ languages** supported\n- Based on PyTorch\u002FTransformers\n\n**Use alternatives instead**:\n- **OpenAI Embeddings**: Need API-based, highest quality\n- **Instructor**: Task-specific instructions\n- **Cohere Embed**: Managed service\n\n## Quick start\n\n### Installation\n\n```bash\npip install sentence-transformers\n```\n\n### Basic usage\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Load model\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Generate embeddings\nsentences = [\n    \"This is an example sentence\",\n    \"Each sentence is converted to a vector\"\n]\n\nembeddings = model.encode(sentences)\nprint(embeddings.shape)  # (2, 384)\n\n# Cosine similarity\nfrom sentence_transformers.util import cos_sim\nsimilarity = cos_sim(embeddings[0], embeddings[1])\nprint(f\"Similarity: {similarity.item():.4f}\")\n```\n\n## Popular models\n\n### General purpose\n\n```python\n# Fast, good quality (384 dim)\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Better quality (768 dim)\nmodel = SentenceTransformer('all-mpnet-base-v2')\n\n# Best quality (1024 dim, slower)\nmodel = SentenceTransformer('all-roberta-large-v1')\n```\n\n### Multilingual\n\n```python\n# 50+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\n\n# 100+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')\n```\n\n### Domain-specific\n\n```python\n# Legal domain\nmodel = SentenceTransformer('nlpaueb\u002Flegal-bert-base-uncased')\n\n# Scientific papers\nmodel = SentenceTransformer('allenai\u002Fspecter')\n\n# Code\nmodel = SentenceTransformer('microsoft\u002Fcodebert-base')\n```\n\n## Semantic search\n\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Corpus\ncorpus = [\n    \"Python is a programming language\",\n    \"Machine learning uses algorithms\",\n    \"Neural networks are powerful\"\n]\n\n# Encode corpus\ncorpus_embeddings = model.encode(corpus, convert_to_tensor=True)\n\n# Query\nquery = \"What is Python?\"\nquery_embedding = model.encode(query, convert_to_tensor=True)\n\n# Find most similar\nhits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)\nprint(hits)\n```\n\n## Similarity computation\n\n```python\n# Cosine similarity\nsimilarity = util.cos_sim(embedding1, embedding2)\n\n# Dot product\nsimilarity = util.dot_score(embedding1, embedding2)\n\n# Pairwise cosine similarity\nsimilarities = util.cos_sim(embeddings, embeddings)\n```\n\n## Batch encoding\n\n```python\n# Efficient batch processing\nsentences = [\"sentence 1\", \"sentence 2\", ...] * 1000\n\nembeddings = model.encode(\n    sentences,\n    batch_size=32,\n    show_progress_bar=True,\n    convert_to_tensor=False  # or True for PyTorch tensors\n)\n```\n\n## Fine-tuning\n\n```python\nfrom sentence_transformers import InputExample, losses\nfrom torch.utils.data import DataLoader\n\n# Training data\ntrain_examples = [\n    InputExample(texts=['sentence 1', 'sentence 2'], label=0.8),\n    InputExample(texts=['sentence 3', 'sentence 4'], label=0.3),\n]\n\ntrain_dataloader = DataLoader(train_examples, batch_size=16)\n\n# Loss function\ntrain_loss = losses.CosineSimilarityLoss(model)\n\n# Train\nmodel.fit(\n    train_objectives=[(train_dataloader, train_loss)],\n    epochs=10,\n    warmup_steps=100\n)\n\n# Save\nmodel.save('my-finetuned-model')\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\n# Use with vector stores\nfrom langchain_chroma import Chroma\n\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=embeddings\n)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\n\nembed_model = HuggingFaceEmbedding(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\nfrom llama_index.core import Settings\nSettings.embed_model = embed_model\n\n# Use in index\nindex = VectorStoreIndex.from_documents(documents)\n```\n\n## Model selection guide\n\n| Model | Dimensions | Speed | Quality | Use Case |\n|-------|------------|-------|---------|----------|\n| all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping |\n| all-mpnet-base-v2 | 768 | Medium | Better | Production RAG |\n| all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed |\n| paraphrase-multilingual | 768 | Medium | Good | Multilingual |\n\n## Best practices\n\n1. **Start with all-MiniLM-L6-v2** - Good baseline\n2. **Normalize embeddings** - Better for cosine similarity\n3. **Use GPU if available** - 10× faster encoding\n4. **Batch encoding** - More efficient\n5. **Cache embeddings** - Expensive to recompute\n6. **Fine-tune for domain** - Improves quality\n7. **Test different models** - Quality varies by task\n8. **Monitor memory** - Large models need more RAM\n\n## Performance\n\n| Model | Speed (sentences\u002Fsec) | Memory | Dimension |\n|-------|----------------------|---------|-----------|\n| MiniLM | ~2000 | 120MB | 384 |\n| MPNet | ~600 | 420MB | 768 |\n| RoBERTa | ~300 | 1.3GB | 1024 |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers ⭐ 15,700+\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fsentence-transformers\n- **Docs**: https:\u002F\u002Fwww.sbert.net\n- **License**: Apache 2.0\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the corpus, embedding model and vector store versions, and where the index will live.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Index a small subset and test retrieval on known question-passage pairs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure recall@k on a labeled set; answers cite retrieved passages only.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Retrieval returns irrelevant chunks | Revisit chunking, add BM25\u002Fhybrid search, or re-rank before changing the generator. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Answers must come from retrieved text; say when the corpus does not contain the answer.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `chroma`: Open-source embedding database for AI applications.\n- `pinecone`: Guides use of Pinecone, a managed serverless vector database, through its Python client and the LangChain and LlamaIndex integrations.\n- `qdrant-vector-search`: High-performance vector similarity search engine for RAG and semantic search.\n",{"name":5,"license":15,"metadata":24,"description":7},{"tags":25,"version":26,"category":27,"maintainer":28,"dependencies":29},"Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production","1.0.0","knowledge-and-rag","Kalaris Labs","sentence-transformers, transformers, torch","main","29fe36d26127205057d5e0ed8425f6b0e9a8c13a","skills\u002Fsentence-transformers",16,"https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills","https:\u002F\u002Fdocs.kalarislabs.com\u002Fresearch-agent-skills","https:\u002F\u002Fgithub.com\u002FKalarisLabs","kalarislabs.com","Applied AI lab building the infrastructure for the agentic era","2026-11-14 17:49:08",{"source":8,"skillName":5,"commands":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add KalarisLabs\u002Fresearch-agent-skills --skill sentence-transformers",{"tool":46,"command":47},"bunx","bunx skills add KalarisLabs\u002Fresearch-agent-skills --skill sentence-transformers",{"tool":49,"command":50},"pnpm","pnpm dlx skills add KalarisLabs\u002Fresearch-agent-skills --skill sentence-transformers","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fsentence-transformers\u002FSKILL.md",true,[54],{"path":55,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":58,"sourceHtmlUrl":59},"references\u002Fmodels.md","REFERENCE","参考文档",2925,"https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fsentence-transformers\u002Freferences\u002Fmodels.md",[61,68,75,82,89,96],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":67,"publishedAt":19},"01M74ZSRSWD92NYM8TNJRH35DK","zarr-python","Guides use of Zarr-Python 3 for storing chunked, compressed N-dimensional arrays and groups, with local, in-memory, ZIP, and fsspec-backed S3\u002FGCS\u002FHTTP stores, plus NumPy, Dask, and Xarray integration. Covers array creation, resizing and appending, attributes, chunk and shard sizing, codecs, consolidated metadata, and v2-to-v3 migration. Use when creating or opening Zarr arrays or groups, choosing chunk sizes or compression for large datasets, reading or writing arrays on S3 or GCS, appending to time-series arrays, or migrating code from Zarr-Python 2 to 3. Use when setting up parallel I\u002FO with Dask or Xarray. For labeled multi-dimensional datasets, use Xarray instead.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fzarr-python\u002FSKILL.md",[],[],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":19},"01M74ZSK6JG5HKNC1AHF4D97G9","whisper","Transcribes and translates audio with OpenAI's Whisper (openai-whisper Python package and whisper CLI), covering model sizes from tiny to large plus turbo, language specification, initial prompts, word and segment timestamps, temperature fallback, batch processing, and subtitle generation. Use when transcribing speech, podcasts, meetings, or video audio to text. Use when translating non-English speech to English. Use when working with noisy or multilingual audio in any of 99 languages. Use when choosing a Whisper model size for available VRAM. Use when generating subtitles from audio. Not for speaker diarization or live captioning; use AssemblyAI or Deepgram instead.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwhisper\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":19},"01M74ZSBYQ2EGDFTTA4ZK6K5TR","weights-and-biases","Logs and tracks machine learning experiments with Weights & Biases (W&B, wandb): metrics, hyperparameters, checkpoints, sweeps, artifacts with lineage, model registry, custom charts and shareable reports. Includes integrations for PyTorch, HuggingFace Transformers, PyTorch Lightning and Keras\u002FTensorFlow. Use when logging training runs and comparing them across configurations, running automated hyperparameter sweeps, versioning datasets and models as artifacts, registering model versions, or sharing results with a team workspace. Use when setting up wandb.init for offline or unstable-connection training. Not for general data versioning outside ML runs.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fweights-and-biases\u002FSKILL.md",[],[],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M74ZS5MZTK84WHEQXCVDZ6GB","waypoint-bio","Use when working with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwaypoint-bio\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":19},"01M74ZS0TCW96WJAKSG07C5Z7A","verl-rl-training","Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fverl-rl-training\u002FSKILL.md",[],[],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":19},"01M74ZRVG6SSWGT1N3Q55134KA","venue-templates","Prepare journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Use when selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.","https:\u002F\u002Fgithub.com\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fvenue-templates\u002FSKILL.md",[],[]]