[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG5ETF6CQKAD226K108G8M":3,"skill-same:01M6XG5ETF6CQKAD226K108G8M":87},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":14,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":66,"install":67,"resources":80},"01M6XG5ETF6CQKAD226K108G8M","pinecone",null,"Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (\u003C100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fpinecone\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"scaling","managed","hybrid","at","Auto-Scaling","Hybrid Search","Managed Service","synthetic","recommendations","latency","rag","auto","vector database","Tools","vector","search","database","infrastructure","semantic search","2026-11-10 10:12:30",false,"---\nname: pinecone\ndescription: Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (\u003C100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [RAG, Pinecone, Vector Database, Managed Service, Serverless, Hybrid Search, Production, Auto-Scaling, Low Latency, Recommendations]\ndependencies: [pinecone-client]\n---\n\n# Pinecone - Managed Vector Database\n\nThe vector database for production AI applications.\n\n## When to use Pinecone\n\n**Use when:**\n- Need managed, serverless vector database\n- Production RAG applications\n- Auto-scaling required\n- Low latency critical (\u003C100ms)\n- Don't want to manage infrastructure\n- Need hybrid search (dense + sparse vectors)\n\n**Metrics**:\n- Fully managed SaaS\n- Auto-scales to billions of vectors\n- **p95 latency \u003C100ms**\n- 99.9% uptime SLA\n\n**Use alternatives instead**:\n- **Chroma**: Self-hosted, open-source\n- **FAISS**: Offline, pure similarity search\n- **Weaviate**: Self-hosted with more features\n\n## Quick start\n\n### Installation\n\n```bash\npip install pinecone-client\n```\n\n### Basic usage\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\n\n# Initialize\npc = Pinecone(api_key=\"your-api-key\")\n\n# Create index\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,  # Must match embedding dimension\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n)\n\n# Connect to index\nindex = pc.Index(\"my-index\")\n\n# Upsert vectors\nindex.upsert(vectors=[\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\n])\n\n# Query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    include_metadata=True\n)\n\nprint(results[\"matches\"])\n```\n\n\n## Credential Setup\n\nCredentials are auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$PINECONE_API_KEY\" ] && echo \"PINECONE_API_KEY set\" || echo \"NOT SET\"\n```\n\nIf not set: connect Pinecone at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\n\n## Core operations\n\n### Create index\n\n```python\n# Serverless (recommended)\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=ServerlessSpec(\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\n        region=\"us-east-1\"\n    )\n)\n\n# Pod-based (for consistent performance)\nfrom pinecone import PodSpec\n\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=PodSpec(\n        environment=\"us-east1-gcp\",\n        pod_type=\"p1.x1\"\n    )\n)\n```\n\n### Upsert vectors\n\n```python\n# Single upsert\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\n        \"metadata\": {\n            \"text\": \"Document content\",\n            \"category\": \"tutorial\",\n            \"timestamp\": \"2025-01-01\"\n        }\n    }\n])\n\n# Batch upsert (recommended)\nvectors = [\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\n]\n\nindex.upsert(vectors=vectors, batch_size=100)\n```\n\n### Query vectors\n\n```python\n# Basic query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=10,\n    include_metadata=True,\n    include_values=False\n)\n\n# With metadata filtering\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\n)\n\n# Namespace query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    namespace=\"production\"\n)\n\n# Access results\nfor match in results[\"matches\"]:\n    print(f\"ID: {match['id']}\")\n    print(f\"Score: {match['score']}\")\n    print(f\"Metadata: {match['metadata']}\")\n```\n\n### Metadata filtering\n\n```python\n# Exact match\nfilter = {\"category\": \"tutorial\"}\n\n# Comparison\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\n\n# Logical operators\nfilter = {\n    \"$and\": [\n        {\"category\": \"tutorial\"},\n        {\"difficulty\": {\"$lte\": 3}}\n    ]\n}  # Also: $or\n\n# In operator\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n```\n\n## Namespaces\n\n```python\n# Partition data by namespace\nindex.upsert(\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\n    namespace=\"user-123\"\n)\n\n# Query specific namespace\nresults = index.query(\n    vector=[...],\n    namespace=\"user-123\",\n    top_k=5\n)\n\n# List namespaces\nstats = index.describe_index_stats()\nprint(stats['namespaces'])\n```\n\n## Hybrid search (dense + sparse)\n\n```python\n# Upsert with sparse vectors\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # Dense vector\n        \"sparse_values\": {\n            \"indices\": [10, 45, 123],  # Token IDs\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\n        },\n        \"metadata\": {\"text\": \"...\"}\n    }\n])\n\n# Hybrid query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    sparse_vector={\n        \"indices\": [10, 45],\n        \"values\": [0.5, 0.3]\n    },\n    top_k=5,\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=\"my-index\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"query\", k=5)\n\n# With metadata filter\nresults = vectorstore.similarity_search(\n    \"query\",\n    k=5,\n    filter={\"category\": \"tutorial\"}\n)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\n\n# Connect to Pinecone\npc = Pinecone(api_key=\"your-key\")\npinecone_index = pc.Index(\"my-index\")\n\n# Create vector store\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n\n# Use in LlamaIndex\nfrom llama_index.core import StorageContext, VectorStoreIndex\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n```\n\n## Index management\n\n```python\n# List indices\nindexes = pc.list_indexes()\n\n# Describe index\nindex_info = pc.describe_index(\"my-index\")\nprint(index_info)\n\n# Get index stats\nstats = index.describe_index_stats()\nprint(f\"Total vectors: {stats['total_vector_count']}\")\nprint(f\"Namespaces: {stats['namespaces']}\")\n\n# Delete index\npc.delete_index(\"my-index\")\n```\n\n## Delete vectors\n\n```python\n# Delete by ID\nindex.delete(ids=[\"vec1\", \"vec2\"])\n\n# Delete by filter\nindex.delete(filter={\"category\": \"old\"})\n\n# Delete all in namespace\nindex.delete(delete_all=True, namespace=\"test\")\n\n# Delete entire index\nindex.delete(delete_all=True)\n```\n\n## Best practices\n\n1. **Use serverless** - Auto-scaling, cost-effective\n2. **Batch upserts** - More efficient (100-200 per batch)\n3. **Add metadata** - Enable filtering\n4. **Use namespaces** - Isolate data by user\u002Ftenant\n5. **Monitor usage** - Check Pinecone dashboard\n6. **Optimize filters** - Index frequently filtered fields\n7. **Test with free tier** - 1 index, 100K vectors free\n8. **Use hybrid search** - Better quality\n9. **Set appropriate dimensions** - Match embedding model\n10. **Regular backups** - Export important data\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Upsert | ~50-100ms | Per batch |\n| Query (p50) | ~50ms | Depends on index size |\n| Query (p95) | ~100ms | SLA target |\n| Metadata filter | ~+10-20ms | Additional overhead |\n\n## Pricing (as of 2025)\n\n**Serverless**:\n- $0.096 per million read units\n- $0.06 per million write units\n- $0.06 per GB storage\u002Fmonth\n\n**Free tier**:\n- 1 serverless index\n- 100K vectors (1536 dimensions)\n- Great for prototyping\n\n## Resources\n\n- **Website**: https:\u002F\u002Fwww.pinecone.io\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **Console**: https:\u002F\u002Fapp.pinecone.io\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\n\n\n","# Pinecone - Managed Vector Database\n\nThe vector database for production AI applications.\n\n## When to use Pinecone\n\n**Use when:**\n- Need managed, serverless vector database\n- Production RAG applications\n- Auto-scaling required\n- Low latency critical (\u003C100ms)\n- Don't want to manage infrastructure\n- Need hybrid search (dense + sparse vectors)\n\n**Metrics**:\n- Fully managed SaaS\n- Auto-scales to billions of vectors\n- **p95 latency \u003C100ms**\n- 99.9% uptime SLA\n\n**Use alternatives instead**:\n- **Chroma**: Self-hosted, open-source\n- **FAISS**: Offline, pure similarity search\n- **Weaviate**: Self-hosted with more features\n\n## Quick start\n\n### Installation\n\n```bash\npip install pinecone-client\n```\n\n### Basic usage\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\n\n# Initialize\npc = Pinecone(api_key=\"your-api-key\")\n\n# Create index\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,  # Must match embedding dimension\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n)\n\n# Connect to index\nindex = pc.Index(\"my-index\")\n\n# Upsert vectors\nindex.upsert(vectors=[\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\n])\n\n# Query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    include_metadata=True\n)\n\nprint(results[\"matches\"])\n```\n\n\n## Credential Setup\n\nCredentials are auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$PINECONE_API_KEY\" ] && echo \"PINECONE_API_KEY set\" || echo \"NOT SET\"\n```\n\nIf not set: connect Pinecone at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\n\n## Core operations\n\n### Create index\n\n```python\n# Serverless (recommended)\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=ServerlessSpec(\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\n        region=\"us-east-1\"\n    )\n)\n\n# Pod-based (for consistent performance)\nfrom pinecone import PodSpec\n\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=PodSpec(\n        environment=\"us-east1-gcp\",\n        pod_type=\"p1.x1\"\n    )\n)\n```\n\n### Upsert vectors\n\n```python\n# Single upsert\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\n        \"metadata\": {\n            \"text\": \"Document content\",\n            \"category\": \"tutorial\",\n            \"timestamp\": \"2025-01-01\"\n        }\n    }\n])\n\n# Batch upsert (recommended)\nvectors = [\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\n]\n\nindex.upsert(vectors=vectors, batch_size=100)\n```\n\n### Query vectors\n\n```python\n# Basic query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=10,\n    include_metadata=True,\n    include_values=False\n)\n\n# With metadata filtering\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\n)\n\n# Namespace query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    namespace=\"production\"\n)\n\n# Access results\nfor match in results[\"matches\"]:\n    print(f\"ID: {match['id']}\")\n    print(f\"Score: {match['score']}\")\n    print(f\"Metadata: {match['metadata']}\")\n```\n\n### Metadata filtering\n\n```python\n# Exact match\nfilter = {\"category\": \"tutorial\"}\n\n# Comparison\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\n\n# Logical operators\nfilter = {\n    \"$and\": [\n        {\"category\": \"tutorial\"},\n        {\"difficulty\": {\"$lte\": 3}}\n    ]\n}  # Also: $or\n\n# In operator\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n```\n\n## Namespaces\n\n```python\n# Partition data by namespace\nindex.upsert(\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\n    namespace=\"user-123\"\n)\n\n# Query specific namespace\nresults = index.query(\n    vector=[...],\n    namespace=\"user-123\",\n    top_k=5\n)\n\n# List namespaces\nstats = index.describe_index_stats()\nprint(stats['namespaces'])\n```\n\n## Hybrid search (dense + sparse)\n\n```python\n# Upsert with sparse vectors\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # Dense vector\n        \"sparse_values\": {\n            \"indices\": [10, 45, 123],  # Token IDs\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\n        },\n        \"metadata\": {\"text\": \"...\"}\n    }\n])\n\n# Hybrid query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    sparse_vector={\n        \"indices\": [10, 45],\n        \"values\": [0.5, 0.3]\n    },\n    top_k=5,\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=\"my-index\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"query\", k=5)\n\n# With metadata filter\nresults = vectorstore.similarity_search(\n    \"query\",\n    k=5,\n    filter={\"category\": \"tutorial\"}\n)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\n\n# Connect to Pinecone\npc = Pinecone(api_key=\"your-key\")\npinecone_index = pc.Index(\"my-index\")\n\n# Create vector store\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n\n# Use in LlamaIndex\nfrom llama_index.core import StorageContext, VectorStoreIndex\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n```\n\n## Index management\n\n```python\n# List indices\nindexes = pc.list_indexes()\n\n# Describe index\nindex_info = pc.describe_index(\"my-index\")\nprint(index_info)\n\n# Get index stats\nstats = index.describe_index_stats()\nprint(f\"Total vectors: {stats['total_vector_count']}\")\nprint(f\"Namespaces: {stats['namespaces']}\")\n\n# Delete index\npc.delete_index(\"my-index\")\n```\n\n## Delete vectors\n\n```python\n# Delete by ID\nindex.delete(ids=[\"vec1\", \"vec2\"])\n\n# Delete by filter\nindex.delete(filter={\"category\": \"old\"})\n\n# Delete all in namespace\nindex.delete(delete_all=True, namespace=\"test\")\n\n# Delete entire index\nindex.delete(delete_all=True)\n```\n\n## Best practices\n\n1. **Use serverless** - Auto-scaling, cost-effective\n2. **Batch upserts** - More efficient (100-200 per batch)\n3. **Add metadata** - Enable filtering\n4. **Use namespaces** - Isolate data by user\u002Ftenant\n5. **Monitor usage** - Check Pinecone dashboard\n6. **Optimize filters** - Index frequently filtered fields\n7. **Test with free tier** - 1 index, 100K vectors free\n8. **Use hybrid search** - Better quality\n9. **Set appropriate dimensions** - Match embedding model\n10. **Regular backups** - Export important data\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Upsert | ~50-100ms | Per batch |\n| Query (p50) | ~50ms | Depends on index size |\n| Query (p95) | ~100ms | SLA target |\n| Metadata filter | ~+10-20ms | Additional overhead |\n\n## Pricing (as of 2025)\n\n**Serverless**:\n- $0.096 per million read units\n- $0.06 per million write units\n- $0.06 per GB storage\u002Fmonth\n\n**Free tier**:\n- 1 serverless index\n- 100K vectors (1536 dimensions)\n- Great for prototyping\n\n## Resources\n\n- **Website**: https:\u002F\u002Fwww.pinecone.io\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **Console**: https:\u002F\u002Fapp.pinecone.io\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\n\n\n",{"name":5,"tags":48,"author":56,"license":15,"version":57,"category":58,"description":7,"dependencies":59},[49,50,51,30,52,29,53,28,54,55],"RAG","Pinecone","Vector Database","Serverless","Production","Low Latency","Recommendations","Synthetic Sciences","1.0.0","llm-tools",[60],"pinecone-client","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fpinecone","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add fadrienne\u002FMyGitHub --skill pinecone",{"tool":73,"command":74},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill pinecone",{"tool":76,"command":77},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill pinecone","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fpinecone\u002FSKILL.md",true,[81],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fdeployment.md","REFERENCE","参考文档",3557,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fpinecone\u002Freferences\u002Fdeployment.md",[88,118,145,179,206,233],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":97,"publishedAt":43},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":127,"publishedAt":43},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[109,128,98,129,130,131,106,132,133,134,135,136,137,138,139,140,141,142,143,144],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"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":160,"publishedAt":43},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[151,154,155],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":156,"categoryName":157,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[161,162,163,164,165,166,167,168,169,170,107,171,172,173,174,175,176,177,133,178],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":189,"publishedAt":43},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[185,188],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},[190,162,191,192,193,194,195,196,197,169,106,198,199,200,201,202,203,204,135,205],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":218,"publishedAt":43},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[212,215],{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":216,"categoryName":217,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000030","开发工程师",[219,220,140,221,222,223,224,105,225,106,107,109,226,227,228,229,230,231,177,232],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":237,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":246,"publishedAt":43},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[240,243,244,245],{"categoryNo":241,"categoryName":242,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":156,"categoryName":157,"taxonomyType":158,"taxonomyTypeDesc":159},[161,247,248,249,250,251,190,252,253,106,107,109,254,255,256,257,258,259,162,177],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]