[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QM2MK5876YVZN5WMVR8427":3,"skill-same:01M6QM2MK5876YVZN5WMVR8427":90},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":14,"repositoryHtmlUrl":64,"repositoryHomepageUrl":65,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6QM2MK5876YVZN5WMVR8427","sentence-transformers",null,"Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-sentence-transformers\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"sentence transformers","Transformers","text","for","orchestra","embedding","domain-specific","domain","multilingual","Art","use","image","clustering","multimodal","embeddings","search","framework","tasks","semantic search","rag","2026-11-10 10:12:30",false,"---\nname: sentence-transformers\ndescription: Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production]\ndependencies: [sentence-transformers, transformers, torch]\ncategory: \"ai-research\"\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- **15,700+ GitHub stars**\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\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- **15,700+ GitHub stars**\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\n",{"name":5,"tags":44,"author":55,"license":15,"version":56,"category":57,"description":7,"dependencies":58},[45,46,47,48,49,50,51,52,53,54],"Sentence Transformers","Embeddings","Semantic Similarity","RAG","Multilingual","Multimodal","Pre-Trained Models","Clustering","Semantic Search","Production","Orchestra Research","1.0.0","ai-research",[5,59,60],"transformers","torch","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Forch-sentence-transformers","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill sentence-transformers",{"tool":76,"command":77},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill sentence-transformers",{"tool":79,"command":80},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill sentence-transformers","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-sentence-transformers\u002FSKILL.md",true,[84],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fmodels.md","REFERENCE","参考文档",2925,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-sentence-transformers\u002Freferences\u002Fmodels.md",[91,121,145,172,197,221],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":39},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[103,104,105,106,34,107,108,109,110,111,112,113,114,115,116,117,22,118,119,120],"youtube full","youtube","full","transcript","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":39},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[128,129,130,131,34,132,133,134,135,136,137,138,139,140,29,141,142,143,144,22],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":154,"publishedAt":39},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[151],{"categoryNo":152,"categoryName":153,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000023","Skill与智能体开发",[155,139,156,157,158,159,160,161,29,162,163,61,164,165,166,167,168,169,170,171],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":39},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[179,180,181,182,183,184,185,186,187,29,188,113,189,190,191,192,193,194,195,196],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":204,"publishedAt":39},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[203],{"categoryNo":152,"categoryName":153,"taxonomyType":100,"taxonomyTypeDesc":101},[205,206,207,208,209,157,184,210,211,212,213,214,139,215,112,216,217,218,219,220],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":227,"publishedAt":39},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[228,229,230,231,232,233,234,235,236,213,237,238,239,240,241,242,114,243,244,196],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]