[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NBEHTQRY8K34P68R87JZF0":3,"skill-same:01M6NBEHTQRY8K34P68R87JZF0":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":60,"commitSha":61,"skillDirectory":62,"forksCount":14,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":6,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6NBEHTQRY8K34P68R87JZF0","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.","zhengliangliahu-cloud\u002Fskills","zhengliangliahu-cloud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F248268067?v=4","01M6NAQ109P2Y11XSV5D9QJFFY","Lin","01M6NAQ15R1M4E18CA2KPJHC06",1,"MIT","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F15-rag\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],"latency","managed","hybrid","for","orchestra","at","Auto-Scaling","Hybrid Search","Managed Service","recommendations","rag","scaling","auto","vector database","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.\nversion: 1.0.0\nauthor: Orchestra Research\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## 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## 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,"description":7,"dependencies":58},[49,50,51,32,52,31,53,30,54,55],"RAG","Pinecone","Vector Database","Serverless","Production","Low Latency","Recommendations","Orchestra Research","1.0.0",[59],"pinecone-client","main","75c7c92896e878938460f0bceb23b6ab4a787286","AI-Research-SKILLs\u002F15-rag\u002Fpinecone","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud","A postgraduate student majoring in mathematics.","2026-06-03 19:04:43",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add zhengliangliahu-cloud\u002Fskills --skill pinecone",{"tool":73,"command":74},"bunx","bunx skills add zhengliangliahu-cloud\u002Fskills --skill 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ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Frigor-reviewer\u002FSKILL.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"reviewer","ara rigor reviewer","ARA","review","release","audit","integrity","peer review","critique","exploration","scoring","dimensions","coherence","structural","scope","native","argument","epistemic","calibration","Research Artifacts",{"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":133,"publishedAt":43},"01M6NBTQTZ5G21FFPMEKHQZ3R3","ara-research-manager","Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara\u002F directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fresearch-manager\u002FSKILL.md",[124,127,128],{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[134,135,136,137,138,139,140,141,142,143,144,145,107,146,147,148,149,29,150,100],"research","manager","ara research manager","writing","directory","heuristics","how","knowledge","knowledge management","never","scanning","conversation","maintain","project","end","recording","claims",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":161,"publishedAt":43},"01M6NBTCRK24WXZYKBD19RAV09","ara-compiler","Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fcompiler\u002FSKILL.md",[157,160],{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},[162,163,164,165,166,167,139,141,168,107,169,170,113,171,172,173,150,100,117,174],"compiler","ara compiler","PDF","notes","code","logs","ingestion","artifact","input","papers","directories","layer","Knowledge Extraction",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":182,"publishedAt":43},"01M6NBSXGH49PE13PF1H9P7F8N","creative-thinking-for-research","Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fcreative-thinking-for-research\u002FSKILL.md",[181],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},[134,183,184,185,186,27,187,188,189,190,191,192,193,194,195,196,197,198,28,199],"creative","thinking","Creative Thinking","creative thinking for research","reasoning","science","use","ideation","frameworks","research ideation","strategies","problem","Analogical Reasoning","Problem Reformulation","novel","directions","constraint",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":207,"publishedAt":43},"01M6NBSNHZYM70ZAFXQNESYYBG","brainstorming-research-ideas","Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fbrainstorming-research-ideas\u002FSKILL.md",[206],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},[134,208,209,210,211,212,213,214,184,191,215,194,216,217,185,218,219,197,198,28],"brainstorming","ideas","brainstorming research ideas","discovery","projects","work","strategy","discover","impact","guides","Problem Discovery","Research Strategy",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":232,"publishedAt":43},"01M6NBS3Z3CJYH5225X73AZN3S","systems-paper-writing","Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F20-ml-paper-writing\u002Fsystems-paper-writing\u002FSKILL.md",[226,227,228,231],{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},[137,233,234,235,236,98,237,238,239,111,240,241,242,243,171,244,28,27,245,246],"Paper Writing","systems","paper","Systems Paper Writing","guide","Blueprint","patterns","OSDI","NSDI","ASPLOS","SOSP","targeting","EuroSys","Structural Blueprint"]