[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JHH0Q7MZEWSWDS3M58QCJY":3,"skill-same:01M6JHH0Q7MZEWSWDS3M58QCJY":94},{"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":26,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":6,"ownerHtmlUrl":71,"ownerBlogUrl":6,"ownerDescription":72,"updatedAt":73,"install":74,"resources":87},"01M6JHH0Q7MZEWSWDS3M58QCJY","chroma",null,"Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"projects","source","embedding","filter","full","local","self","vector database","text","rag","open","api","Transformers","open source","embeddings","vector","search","database","semantic search","2026-11-10 10:12:30",false,"---\nname: chroma\ndescription: Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [RAG, Chroma, Vector Database, Embeddings, Semantic Search, Open Source, Self-Hosted, Document Retrieval, Metadata Filtering]\ndependencies: [chromadb, sentence-transformers]\n---\n\n# Chroma - Open-Source Embedding Database\n\nThe AI-native database for building LLM applications with memory.\n\n## When to use Chroma\n\n**Use Chroma when:**\n- Building RAG (retrieval-augmented generation) applications\n- Need local\u002Fself-hosted vector database\n- Want open-source solution (Apache 2.0)\n- Prototyping in notebooks\n- Semantic search over documents\n- Storing embeddings with metadata\n\n**Metrics**:\n- **24,300+ GitHub stars**\n- **1,900+ forks**\n- **v1.3.3** (stable, weekly releases)\n- **Apache 2.0 license**\n\n**Use alternatives instead**:\n- **Pinecone**: Managed cloud, auto-scaling\n- **FAISS**: Pure similarity search, no metadata\n- **Weaviate**: Production ML-native database\n- **Qdrant**: High performance, Rust-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install chromadb\n\n# JavaScript\u002FTypeScript\nnpm install chromadb @chroma-core\u002Fdefault-embed\n```\n\n### Basic usage (Python)\n\n```python\nimport chromadb\n\n# Create client\nclient = chromadb.Client()\n\n# Create collection\ncollection = client.create_collection(name=\"my_collection\")\n\n# Add documents\ncollection.add(\n    documents=[\"This is document 1\", \"This is document 2\"],\n    metadatas=[{\"source\": \"doc1\"}, {\"source\": \"doc2\"}],\n    ids=[\"id1\", \"id2\"]\n)\n\n# Query\nresults = collection.query(\n    query_texts=[\"document about topic\"],\n    n_results=2\n)\n\nprint(results)\n```\n\n## Core operations\n\n### 1. Create collection\n\n```python\n# Simple collection\ncollection = client.create_collection(\"my_docs\")\n\n# With custom embedding function\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"my_docs\",\n    embedding_function=openai_ef\n)\n\n# Get existing collection\ncollection = client.get_collection(\"my_docs\")\n\n# Delete collection\nclient.delete_collection(\"my_docs\")\n```\n\n### 2. Add documents\n\n```python\n# Add with auto-generated IDs\ncollection.add(\n    documents=[\"Doc 1\", \"Doc 2\", \"Doc 3\"],\n    metadatas=[\n        {\"source\": \"web\", \"category\": \"tutorial\"},\n        {\"source\": \"pdf\", \"page\": 5},\n        {\"source\": \"api\", \"timestamp\": \"2025-01-01\"}\n    ],\n    ids=[\"id1\", \"id2\", \"id3\"]\n)\n\n# Add with custom embeddings\ncollection.add(\n    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],\n    documents=[\"Doc 1\", \"Doc 2\"],\n    ids=[\"id1\", \"id2\"]\n)\n```\n\n### 3. Query (similarity search)\n\n```python\n# Basic query\nresults = collection.query(\n    query_texts=[\"machine learning tutorial\"],\n    n_results=5\n)\n\n# Query with filters\nresults = collection.query(\n    query_texts=[\"Python programming\"],\n    n_results=3,\n    where={\"source\": \"web\"}\n)\n\n# Query with metadata filters\nresults = collection.query(\n    query_texts=[\"advanced topics\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$gte\": 3}}\n        ]\n    }\n)\n\n# Access results\nprint(results[\"documents\"])      # List of matching documents\nprint(results[\"metadatas\"])      # Metadata for each doc\nprint(results[\"distances\"])      # Similarity scores\nprint(results[\"ids\"])            # Document IDs\n```\n\n### 4. Get documents\n\n```python\n# Get by IDs\ndocs = collection.get(\n    ids=[\"id1\", \"id2\"]\n)\n\n# Get with filters\ndocs = collection.get(\n    where={\"category\": \"tutorial\"},\n    limit=10\n)\n\n# Get all documents\ndocs = collection.get()\n```\n\n### 5. Update documents\n\n```python\n# Update document content\ncollection.update(\n    ids=[\"id1\"],\n    documents=[\"Updated content\"],\n    metadatas=[{\"source\": \"updated\"}]\n)\n```\n\n### 6. Delete documents\n\n```python\n# Delete by IDs\ncollection.delete(ids=[\"id1\", \"id2\"])\n\n# Delete with filter\ncollection.delete(\n    where={\"source\": \"outdated\"}\n)\n```\n\n## Persistent storage\n\n```python\n# Persist to disk\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\ncollection = client.create_collection(\"my_docs\")\ncollection.add(documents=[\"Doc 1\"], ids=[\"id1\"])\n\n# Data persisted automatically\n# Reload later with same path\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = client.get_collection(\"my_docs\")\n```\n\n## Embedding functions\n\n### Default (Sentence Transformers)\n\n```python\n# Uses sentence-transformers by default\ncollection = client.create_collection(\"my_docs\")\n# Default model: all-MiniLM-L6-v2\n```\n\n### OpenAI\n\n```python\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"openai_docs\",\n    embedding_function=openai_ef\n)\n```\n\n### HuggingFace\n\n```python\nhuggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\ncollection = client.create_collection(\n    name=\"hf_docs\",\n    embedding_function=huggingface_ef\n)\n```\n\n### Custom embedding function\n\n```python\nfrom chromadb import Documents, EmbeddingFunction, Embeddings\n\nclass MyEmbeddingFunction(EmbeddingFunction):\n    def __call__(self, input: Documents) -> Embeddings:\n        # Your embedding logic\n        return embeddings\n\nmy_ef = MyEmbeddingFunction()\ncollection = client.create_collection(\n    name=\"custom_docs\",\n    embedding_function=my_ef\n)\n```\n\n## Metadata filtering\n\n```python\n# Exact match\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"category\": \"tutorial\"}\n)\n\n# Comparison operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"page\": {\"$gt\": 10}}  # $gt, $gte, $lt, $lte, $ne\n)\n\n# Logical operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$lte\": 3}}\n        ]\n    }  # Also: $or\n)\n\n# Contains\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\n# Split documents\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\ndocs = text_splitter.split_documents(documents)\n\n# Create Chroma vector store\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    persist_directory=\".\u002Fchroma_db\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"machine learning\", k=3)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.chroma import ChromaVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\nimport chromadb\n\n# Initialize Chroma\ndb = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = db.get_or_create_collection(\"my_collection\")\n\n# Create vector store\nvector_store = ChromaVectorStore(chroma_collection=collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\n# Create index\nindex = VectorStoreIndex.from_documents(\n    documents,\n    storage_context=storage_context\n)\n\n# Query\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What is machine learning?\")\n```\n\n## Server mode\n\n```python\n# Run Chroma server\n# Terminal: chroma run --path .\u002Fchroma_db --port 8000\n\n# Connect to server\nimport chromadb\nfrom chromadb.config import Settings\n\nclient = chromadb.HttpClient(\n    host=\"localhost\",\n    port=8000,\n    settings=Settings(anonymized_telemetry=False)\n)\n\n# Use as normal\ncollection = client.get_or_create_collection(\"my_docs\")\n```\n\n## Best practices\n\n1. **Use persistent client** - Don't lose data on restart\n2. **Add metadata** - Enables filtering and tracking\n3. **Batch operations** - Add multiple docs at once\n4. **Choose right embedding model** - Balance speed\u002Fquality\n5. **Use filters** - Narrow search space\n6. **Unique IDs** - Avoid collisions\n7. **Regular backups** - Copy chroma_db directory\n8. **Monitor collection size** - Scale up if needed\n9. **Test embedding functions** - Ensure quality\n10. **Use server mode for production** - Better for multi-user\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Add 100 docs | ~1-3s | With embedding |\n| Query (top 10) | ~50-200ms | Depends on collection size |\n| Metadata filter | ~10-50ms | Fast with proper indexing |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma ⭐ 24,300+\n- **Docs**: https:\u002F\u002Fdocs.trychroma.com\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FMMeYNTmh3x\n- **Version**: 1.3.3+\n- **License**: Apache 2.0\n\n\n","# Chroma - Open-Source Embedding Database\n\nThe AI-native database for building LLM applications with memory.\n\n## When to use Chroma\n\n**Use Chroma when:**\n- Building RAG (retrieval-augmented generation) applications\n- Need local\u002Fself-hosted vector database\n- Want open-source solution (Apache 2.0)\n- Prototyping in notebooks\n- Semantic search over documents\n- Storing embeddings with metadata\n\n**Metrics**:\n- **24,300+ GitHub stars**\n- **1,900+ forks**\n- **v1.3.3** (stable, weekly releases)\n- **Apache 2.0 license**\n\n**Use alternatives instead**:\n- **Pinecone**: Managed cloud, auto-scaling\n- **FAISS**: Pure similarity search, no metadata\n- **Weaviate**: Production ML-native database\n- **Qdrant**: High performance, Rust-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install chromadb\n\n# JavaScript\u002FTypeScript\nnpm install chromadb @chroma-core\u002Fdefault-embed\n```\n\n### Basic usage (Python)\n\n```python\nimport chromadb\n\n# Create client\nclient = chromadb.Client()\n\n# Create collection\ncollection = client.create_collection(name=\"my_collection\")\n\n# Add documents\ncollection.add(\n    documents=[\"This is document 1\", \"This is document 2\"],\n    metadatas=[{\"source\": \"doc1\"}, {\"source\": \"doc2\"}],\n    ids=[\"id1\", \"id2\"]\n)\n\n# Query\nresults = collection.query(\n    query_texts=[\"document about topic\"],\n    n_results=2\n)\n\nprint(results)\n```\n\n## Core operations\n\n### 1. Create collection\n\n```python\n# Simple collection\ncollection = client.create_collection(\"my_docs\")\n\n# With custom embedding function\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"my_docs\",\n    embedding_function=openai_ef\n)\n\n# Get existing collection\ncollection = client.get_collection(\"my_docs\")\n\n# Delete collection\nclient.delete_collection(\"my_docs\")\n```\n\n### 2. Add documents\n\n```python\n# Add with auto-generated IDs\ncollection.add(\n    documents=[\"Doc 1\", \"Doc 2\", \"Doc 3\"],\n    metadatas=[\n        {\"source\": \"web\", \"category\": \"tutorial\"},\n        {\"source\": \"pdf\", \"page\": 5},\n        {\"source\": \"api\", \"timestamp\": \"2025-01-01\"}\n    ],\n    ids=[\"id1\", \"id2\", \"id3\"]\n)\n\n# Add with custom embeddings\ncollection.add(\n    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],\n    documents=[\"Doc 1\", \"Doc 2\"],\n    ids=[\"id1\", \"id2\"]\n)\n```\n\n### 3. Query (similarity search)\n\n```python\n# Basic query\nresults = collection.query(\n    query_texts=[\"machine learning tutorial\"],\n    n_results=5\n)\n\n# Query with filters\nresults = collection.query(\n    query_texts=[\"Python programming\"],\n    n_results=3,\n    where={\"source\": \"web\"}\n)\n\n# Query with metadata filters\nresults = collection.query(\n    query_texts=[\"advanced topics\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$gte\": 3}}\n        ]\n    }\n)\n\n# Access results\nprint(results[\"documents\"])      # List of matching documents\nprint(results[\"metadatas\"])      # Metadata for each doc\nprint(results[\"distances\"])      # Similarity scores\nprint(results[\"ids\"])            # Document IDs\n```\n\n### 4. Get documents\n\n```python\n# Get by IDs\ndocs = collection.get(\n    ids=[\"id1\", \"id2\"]\n)\n\n# Get with filters\ndocs = collection.get(\n    where={\"category\": \"tutorial\"},\n    limit=10\n)\n\n# Get all documents\ndocs = collection.get()\n```\n\n### 5. Update documents\n\n```python\n# Update document content\ncollection.update(\n    ids=[\"id1\"],\n    documents=[\"Updated content\"],\n    metadatas=[{\"source\": \"updated\"}]\n)\n```\n\n### 6. Delete documents\n\n```python\n# Delete by IDs\ncollection.delete(ids=[\"id1\", \"id2\"])\n\n# Delete with filter\ncollection.delete(\n    where={\"source\": \"outdated\"}\n)\n```\n\n## Persistent storage\n\n```python\n# Persist to disk\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\ncollection = client.create_collection(\"my_docs\")\ncollection.add(documents=[\"Doc 1\"], ids=[\"id1\"])\n\n# Data persisted automatically\n# Reload later with same path\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = client.get_collection(\"my_docs\")\n```\n\n## Embedding functions\n\n### Default (Sentence Transformers)\n\n```python\n# Uses sentence-transformers by default\ncollection = client.create_collection(\"my_docs\")\n# Default model: all-MiniLM-L6-v2\n```\n\n### OpenAI\n\n```python\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"openai_docs\",\n    embedding_function=openai_ef\n)\n```\n\n### HuggingFace\n\n```python\nhuggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\ncollection = client.create_collection(\n    name=\"hf_docs\",\n    embedding_function=huggingface_ef\n)\n```\n\n### Custom embedding function\n\n```python\nfrom chromadb import Documents, EmbeddingFunction, Embeddings\n\nclass MyEmbeddingFunction(EmbeddingFunction):\n    def __call__(self, input: Documents) -> Embeddings:\n        # Your embedding logic\n        return embeddings\n\nmy_ef = MyEmbeddingFunction()\ncollection = client.create_collection(\n    name=\"custom_docs\",\n    embedding_function=my_ef\n)\n```\n\n## Metadata filtering\n\n```python\n# Exact match\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"category\": \"tutorial\"}\n)\n\n# Comparison operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"page\": {\"$gt\": 10}}  # $gt, $gte, $lt, $lte, $ne\n)\n\n# Logical operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$lte\": 3}}\n        ]\n    }  # Also: $or\n)\n\n# Contains\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\n# Split documents\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\ndocs = text_splitter.split_documents(documents)\n\n# Create Chroma vector store\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    persist_directory=\".\u002Fchroma_db\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"machine learning\", k=3)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.chroma import ChromaVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\nimport chromadb\n\n# Initialize Chroma\ndb = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = db.get_or_create_collection(\"my_collection\")\n\n# Create vector store\nvector_store = ChromaVectorStore(chroma_collection=collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\n# Create index\nindex = VectorStoreIndex.from_documents(\n    documents,\n    storage_context=storage_context\n)\n\n# Query\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What is machine learning?\")\n```\n\n## Server mode\n\n```python\n# Run Chroma server\n# Terminal: chroma run --path .\u002Fchroma_db --port 8000\n\n# Connect to server\nimport chromadb\nfrom chromadb.config import Settings\n\nclient = chromadb.HttpClient(\n    host=\"localhost\",\n    port=8000,\n    settings=Settings(anonymized_telemetry=False)\n)\n\n# Use as normal\ncollection = client.get_or_create_collection(\"my_docs\")\n```\n\n## Best practices\n\n1. **Use persistent client** - Don't lose data on restart\n2. **Add metadata** - Enables filtering and tracking\n3. **Batch operations** - Add multiple docs at once\n4. **Choose right embedding model** - Balance speed\u002Fquality\n5. **Use filters** - Narrow search space\n6. **Unique IDs** - Avoid collisions\n7. **Regular backups** - Copy chroma_db directory\n8. **Monitor collection size** - Scale up if needed\n9. **Test embedding functions** - Ensure quality\n10. **Use server mode for production** - Better for multi-user\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Add 100 docs | ~1-3s | With embedding |\n| Query (top 10) | ~50-200ms | Depends on collection size |\n| Metadata filter | ~10-50ms | Fast with proper indexing |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma ⭐ 24,300+\n- **Docs**: https:\u002F\u002Fdocs.trychroma.com\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FMMeYNTmh3x\n- **Version**: 1.3.3+\n- **License**: Apache 2.0\n\n\n",{"name":5,"tags":51,"author":61,"license":15,"version":62,"description":7,"dependencies":63},[52,53,54,55,56,57,58,59,60],"RAG","Chroma","Vector Database","Embeddings","Semantic Search","Open Source","Self-Hosted","Document Retrieval","Metadata Filtering","Orchestra Research","1.0.0",[64,65],"chromadb","sentence-transformers","main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":86},[76,79,82],{"tool":77,"command":78},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill chroma",{"tool":80,"command":81},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill chroma",{"tool":83,"command":84},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill chroma","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma\u002FSKILL.md",true,[88],{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fintegration.md","REFERENCE","参考文档",771,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma\u002Freferences\u002Fintegration.md",[95,125,145,169,189,214],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":104,"publishedAt":46},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","platform","apps","how to","No-Code","which","for","spawner",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":46},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[127,132,133,134,135,136,137,138,139,37,140,141,115,142,143,144],"cli","github","commit","pull request","openai","pull","push","flow","gh","user","only","stage","github cli",{"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":152,"publishedAt":46},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[151],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},[110,111,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,123,124],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":46},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[176,177,178,179,180,156,181,157,158,182,115,183,184,185,186,187,188,168,123,124],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":198,"publishedAt":46},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[195],{"categoryNo":196,"categoryName":197,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[199,200,201,168,202,110,203,204,205,206,27,207,115,208,209,210,211,212,213,123],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","work","manus","structured output","progress","transforms","multi","output",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":222,"publishedAt":46},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[220,221],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},[110,223,163,224,106,225,226,156,227,204,200,44,38,228,229,230,231,232,233,234],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]