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Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1986,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\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",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"text","Clusters","source","embedding","filter","full","local","self","self-hosted","vector database","rag","projects","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":53,"author":63,"license":15,"version":64,"description":7,"dependencies":65},[54,55,56,57,58,59,60,61,62],"RAG","Chroma","Vector Database","Embeddings","Semantic Search","Open Source","Self-Hosted","Document Retrieval","Metadata Filtering","Orchestra Research","1.0.0",[66,67],"chromadb","sentence-transformers","main","90b50ed0778745a80fd5bfb13b1843d45b9e82f7","tests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma",233,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-13 03:33:43",{"source":8,"skillName":5,"commands":78,"prompt":88,"zipAvailable":89},[79,82,85],{"tool":80,"command":81},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill chroma",{"tool":83,"command":84},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill chroma",{"tool":86,"command":87},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill chroma","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma\u002FSKILL.md",true,[91],{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fintegration.md","REFERENCE","参考文档",771,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-chroma\u002Freferences\u002Fintegration.md",[98,137,161,187,214,235],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":103,"sourceHtmlUrl":104,"categories":105,"keywords":109,"publishedAt":48},"01M6FEGPPK0ZTKCCM0R3E3KS33","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","component","routing","state management","state","shadcn","file","ui","Tools","Tailwind","use","html","single-file","simple","ui components","multi","for","jsx","suite",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":148,"publishedAt":48},"01M6FEE9VTK1W4GY21AKREJXRG","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[144,147],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},[149,150,151,115,152,153,111,154,155,156,125,157,158,159,33,160,134],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","behavior","with",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":165,"sourceHtmlUrl":166,"categories":167,"keywords":168,"publishedAt":48},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[169,170,171,172,173,39,174,175,128,176,177,178,179,180,181,182,160,183,184,185,134,186],"analysis","neuropixels analysis","spike","metrics","data","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load","when",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":191,"sourceHtmlUrl":192,"categories":193,"keywords":197,"publishedAt":48},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[194],{"categoryNo":195,"categoryName":196,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[198,199,200,201,202,203,204,205,206,207,208,209,210,211,160,134,212,186,213],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"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":218,"sourceHtmlUrl":219,"categories":220,"keywords":222,"publishedAt":48},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[221],{"categoryNo":195,"categoryName":196,"taxonomyType":21,"taxonomyTypeDesc":22},[216,223,224,225,226,227,228,156,229,230,128,231,232,233,234,134,29],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":239,"sourceHtmlUrl":240,"categories":241,"keywords":248,"publishedAt":48},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[242,245],{"categoryNo":243,"categoryName":244,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":246,"categoryName":247,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[249,250,251,199,252,126,253,254,204,128,255,211,256,257,258,134,259,186],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]