[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MX0B4QXSH87DFMNANEZDYZ":3,"skill-same:01M6MX0B4QXSH87DFMNANEZDYZ":78},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6MX0B4QXSH87DFMNANEZDYZ","ai-rag-pipeline",null,"Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.\nTools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter.\nCapabilities: research, fact-checking, grounded responses, knowledge retrieval.\nUse for: AI agents, research assistants, fact-checkers, knowledge bases.\nTriggers: rag, retrieval augmented generation, grounded ai, search and answer,\nresearch agent, fact checking, knowledge retrieval, ai research, search + llm,\nweb grounded, perplexity alternative, ai with sources, citation, research pipeline","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fai-rag-pipeline\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"rag","rag pipeline","ai rag pipeline","pipeline","research agent","research pipeline","fact-checking","sources","openrouter","web search","knowledge","Bash","tavily","exa search","Tools","perplexity","search","capabilities","llms","ai agents","2026-11-10 10:12:30",false,"---\nname: ai-rag-pipeline\ndescription: |\n  Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.\n  Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter.\n  Capabilities: research, fact-checking, grounded responses, knowledge retrieval.\n  Use for: AI agents, research assistants, fact-checkers, knowledge bases.\n  Triggers: rag, retrieval augmented generation, grounded ai, search and answer,\n  research agent, fact checking, knowledge retrieval, ai research, search + llm,\n  web grounded, perplexity alternative, ai with sources, citation, research pipeline\nallowed-tools: Bash(infsh *)\n---\n\n# AI RAG Pipeline\n\nBuild RAG (Retrieval Augmented Generation) pipelines via [inference.sh](https:\u002F\u002Finference.sh) CLI.\n\n## Quick Start\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.inference.sh | sh && infsh login\n\n# Simple RAG: Search + LLM\nSEARCH=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"latest AI developments 2024\"}')\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Based on this research, summarize the key trends: $SEARCH\\\"\n}\"\n```\n\n## What is RAG?\n\nRAG combines:\n1. **Retrieval**: Fetch relevant information from external sources\n2. **Augmentation**: Add retrieved context to the prompt\n3. **Generation**: LLM generates response using the context\n\nThis produces more accurate, up-to-date, and verifiable AI responses.\n\n## RAG Pipeline Patterns\n\n### Pattern 1: Simple Search + Answer\n\n```\n[User Query] -> [Web Search] -> [LLM with Context] -> [Answer]\n```\n\n### Pattern 2: Multi-Source Research\n\n```\n[Query] -> [Multiple Searches] -> [Aggregate] -> [LLM Analysis] -> [Report]\n```\n\n### Pattern 3: Extract + Process\n\n```\n[URLs] -> [Content Extraction] -> [Chunking] -> [LLM Summary] -> [Output]\n```\n\n## Available Tools\n\n### Search Tools\n\n| Tool | App ID | Best For |\n|------|--------|----------|\n| Tavily Search | `tavily\u002Fsearch-assistant` | AI-powered search with answers |\n| Exa Search | `exa\u002Fsearch` | Neural search, semantic matching |\n| Exa Answer | `exa\u002Fanswer` | Direct factual answers |\n\n### Extraction Tools\n\n| Tool | App ID | Best For |\n|------|--------|----------|\n| Tavily Extract | `tavily\u002Fextract` | Clean content from URLs |\n| Exa Extract | `exa\u002Fextract` | Analyze web content |\n\n### LLM Tools\n\n| Model | App ID | Best For |\n|-------|--------|----------|\n| Claude Sonnet 4.5 | `openrouter\u002Fclaude-sonnet-45` | Complex analysis |\n| Claude Haiku 4.5 | `openrouter\u002Fclaude-haiku-45` | Fast processing |\n| GPT-4o | `openrouter\u002Fgpt-4o` | General purpose |\n| Gemini 2.5 Pro | `openrouter\u002Fgemini-25-pro` | Long context |\n\n## Pipeline Examples\n\n### Basic RAG Pipeline\n\n```bash\n# 1. Search for information\nSEARCH_RESULT=$(infsh app run tavily\u002Fsearch-assistant --input '{\n  \"query\": \"What are the latest breakthroughs in quantum computing 2024?\"\n}')\n\n# 2. Generate grounded response\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"You are a research assistant. Based on the following search results, provide a comprehensive summary with citations.\n\nSearch Results:\n$SEARCH_RESULT\n\nProvide a well-structured summary with source citations.\\\"\n}\"\n```\n\n### Multi-Source Research\n\n```bash\n# Search multiple sources\nTAVILY=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"electric vehicle market trends 2024\"}')\nEXA=$(infsh app run exa\u002Fsearch --input '{\"query\": \"EV market analysis latest reports\"}')\n\n# Combine and analyze\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Analyze these research results and identify common themes and contradictions.\n\nSource 1 (Tavily):\n$TAVILY\n\nSource 2 (Exa):\n$EXA\n\nProvide a balanced analysis with sources.\\\"\n}\"\n```\n\n### URL Content Analysis\n\n```bash\n# 1. Extract content from specific URLs\nCONTENT=$(infsh app run tavily\u002Fextract --input '{\n  \"urls\": [\n    \"https:\u002F\u002Fexample.com\u002Fresearch-paper\",\n    \"https:\u002F\u002Fexample.com\u002Findustry-report\"\n  ]\n}')\n\n# 2. Analyze extracted content\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Analyze these documents and extract key insights:\n\n$CONTENT\n\nProvide:\n1. Key findings\n2. Data points\n3. Recommendations\\\"\n}\"\n```\n\n### Fact-Checking Pipeline\n\n```bash\n# Claim to verify\nCLAIM=\"AI will replace 50% of jobs by 2030\"\n\n# 1. Search for evidence\nEVIDENCE=$(infsh app run tavily\u002Fsearch-assistant --input \"{\n  \\\"query\\\": \\\"$CLAIM evidence studies research\\\"\n}\")\n\n# 2. Verify claim\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Fact-check this claim: '$CLAIM'\n\nBased on the following evidence:\n$EVIDENCE\n\nProvide:\n1. Verdict (True\u002FFalse\u002FPartially True\u002FUnverified)\n2. Supporting evidence\n3. Contradicting evidence\n4. Sources\\\"\n}\"\n```\n\n### Research Report Generator\n\n```bash\nTOPIC=\"Impact of generative AI on creative industries\"\n\n# 1. Initial research\nOVERVIEW=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$TOPIC overview\\\"}\")\nSTATISTICS=$(infsh app run exa\u002Fsearch --input \"{\\\"query\\\": \\\"$TOPIC statistics data\\\"}\")\nOPINIONS=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$TOPIC expert opinions\\\"}\")\n\n# 2. Generate comprehensive report\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Generate a comprehensive research report on: $TOPIC\n\nResearch Data:\n== Overview ==\n$OVERVIEW\n\n== Statistics ==\n$STATISTICS\n\n== Expert Opinions ==\n$OPINIONS\n\nFormat as a professional report with:\n- Executive Summary\n- Key Findings\n- Data Analysis\n- Expert Perspectives\n- Conclusion\n- Sources\\\"\n}\"\n```\n\n### Quick Answer with Sources\n\n```bash\n# Use Exa Answer for direct factual questions\ninfsh app run exa\u002Fanswer --input '{\n  \"question\": \"What is the current market cap of NVIDIA?\"\n}'\n```\n\n## Best Practices\n\n### 1. Query Optimization\n\n```bash\n# Bad: Too vague\n\"AI news\"\n\n# Good: Specific and contextual\n\"latest developments in large language models January 2024\"\n```\n\n### 2. Context Management\n\n```bash\n# Summarize long search results before sending to LLM\nSEARCH=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"...\"}')\n\n# If too long, summarize first\nSUMMARY=$(infsh app run openrouter\u002Fclaude-haiku-45 --input \"{\n  \\\"prompt\\\": \\\"Summarize these search results in bullet points: $SEARCH\\\"\n}\")\n\n# Then use summary for analysis\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Based on this research summary, provide insights: $SUMMARY\\\"\n}\"\n```\n\n### 3. Source Attribution\n\nAlways ask the LLM to cite sources:\n\n```bash\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input '{\n  \"prompt\": \"... Always cite sources in [Source Name](URL) format.\"\n}'\n```\n\n### 4. Iterative Research\n\n```bash\n# First pass: broad search\nINITIAL=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"topic overview\"}')\n\n# Second pass: dive deeper based on findings\nDEEP=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"specific aspect from initial search\"}')\n```\n\n## Pipeline Templates\n\n### Agent Research Tool\n\n```bash\n#!\u002Fbin\u002Fbash\n# research.sh - Reusable research function\n\nresearch() {\n  local query=\"$1\"\n\n  # Search\n  local results=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$query\\\"}\")\n\n  # Analyze\n  infsh app run openrouter\u002Fclaude-haiku-45 --input \"{\n    \\\"prompt\\\": \\\"Summarize: $results\\\"\n  }\"\n}\n\nresearch \"your query here\"\n```\n\n## Related Skills\n\n```bash\n# Web search tools\nnpx skills add inference-sh\u002Fagent-skills@web-search\n\n# LLM models\nnpx skills add inference-sh\u002Fagent-skills@llm-models\n\n# Content pipelines\nnpx skills add inference-sh\u002Fagent-skills@ai-content-pipeline\n\n# Full platform skill\nnpx skills add inference-sh\u002Fagent-skills@inference-sh\n```\n\nBrowse all apps: `infsh app list`\n\n## Documentation\n\n- [Adding Tools to Agents](https:\u002F\u002Finference.sh\u002Fdocs\u002Fagents\u002Fadding-tools) - Agent tool integration\n- [Building a Research Agent](https:\u002F\u002Finference.sh\u002Fblog\u002Fguides\u002Fresearch-agent) - Full guide\n","# AI RAG Pipeline\n\nBuild RAG (Retrieval Augmented Generation) pipelines via [inference.sh](https:\u002F\u002Finference.sh) CLI.\n\n## Quick Start\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.inference.sh | sh && infsh login\n\n# Simple RAG: Search + LLM\nSEARCH=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"latest AI developments 2024\"}')\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Based on this research, summarize the key trends: $SEARCH\\\"\n}\"\n```\n\n## What is RAG?\n\nRAG combines:\n1. **Retrieval**: Fetch relevant information from external sources\n2. **Augmentation**: Add retrieved context to the prompt\n3. **Generation**: LLM generates response using the context\n\nThis produces more accurate, up-to-date, and verifiable AI responses.\n\n## RAG Pipeline Patterns\n\n### Pattern 1: Simple Search + Answer\n\n```\n[User Query] -> [Web Search] -> [LLM with Context] -> [Answer]\n```\n\n### Pattern 2: Multi-Source Research\n\n```\n[Query] -> [Multiple Searches] -> [Aggregate] -> [LLM Analysis] -> [Report]\n```\n\n### Pattern 3: Extract + Process\n\n```\n[URLs] -> [Content Extraction] -> [Chunking] -> [LLM Summary] -> [Output]\n```\n\n## Available Tools\n\n### Search Tools\n\n| Tool | App ID | Best For |\n|------|--------|----------|\n| Tavily Search | `tavily\u002Fsearch-assistant` | AI-powered search with answers |\n| Exa Search | `exa\u002Fsearch` | Neural search, semantic matching |\n| Exa Answer | `exa\u002Fanswer` | Direct factual answers |\n\n### Extraction Tools\n\n| Tool | App ID | Best For |\n|------|--------|----------|\n| Tavily Extract | `tavily\u002Fextract` | Clean content from URLs |\n| Exa Extract | `exa\u002Fextract` | Analyze web content |\n\n### LLM Tools\n\n| Model | App ID | Best For |\n|-------|--------|----------|\n| Claude Sonnet 4.5 | `openrouter\u002Fclaude-sonnet-45` | Complex analysis |\n| Claude Haiku 4.5 | `openrouter\u002Fclaude-haiku-45` | Fast processing |\n| GPT-4o | `openrouter\u002Fgpt-4o` | General purpose |\n| Gemini 2.5 Pro | `openrouter\u002Fgemini-25-pro` | Long context |\n\n## Pipeline Examples\n\n### Basic RAG Pipeline\n\n```bash\n# 1. Search for information\nSEARCH_RESULT=$(infsh app run tavily\u002Fsearch-assistant --input '{\n  \"query\": \"What are the latest breakthroughs in quantum computing 2024?\"\n}')\n\n# 2. Generate grounded response\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"You are a research assistant. Based on the following search results, provide a comprehensive summary with citations.\n\nSearch Results:\n$SEARCH_RESULT\n\nProvide a well-structured summary with source citations.\\\"\n}\"\n```\n\n### Multi-Source Research\n\n```bash\n# Search multiple sources\nTAVILY=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"electric vehicle market trends 2024\"}')\nEXA=$(infsh app run exa\u002Fsearch --input '{\"query\": \"EV market analysis latest reports\"}')\n\n# Combine and analyze\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Analyze these research results and identify common themes and contradictions.\n\nSource 1 (Tavily):\n$TAVILY\n\nSource 2 (Exa):\n$EXA\n\nProvide a balanced analysis with sources.\\\"\n}\"\n```\n\n### URL Content Analysis\n\n```bash\n# 1. Extract content from specific URLs\nCONTENT=$(infsh app run tavily\u002Fextract --input '{\n  \"urls\": [\n    \"https:\u002F\u002Fexample.com\u002Fresearch-paper\",\n    \"https:\u002F\u002Fexample.com\u002Findustry-report\"\n  ]\n}')\n\n# 2. Analyze extracted content\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Analyze these documents and extract key insights:\n\n$CONTENT\n\nProvide:\n1. Key findings\n2. Data points\n3. Recommendations\\\"\n}\"\n```\n\n### Fact-Checking Pipeline\n\n```bash\n# Claim to verify\nCLAIM=\"AI will replace 50% of jobs by 2030\"\n\n# 1. Search for evidence\nEVIDENCE=$(infsh app run tavily\u002Fsearch-assistant --input \"{\n  \\\"query\\\": \\\"$CLAIM evidence studies research\\\"\n}\")\n\n# 2. Verify claim\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Fact-check this claim: '$CLAIM'\n\nBased on the following evidence:\n$EVIDENCE\n\nProvide:\n1. Verdict (True\u002FFalse\u002FPartially True\u002FUnverified)\n2. Supporting evidence\n3. Contradicting evidence\n4. Sources\\\"\n}\"\n```\n\n### Research Report Generator\n\n```bash\nTOPIC=\"Impact of generative AI on creative industries\"\n\n# 1. Initial research\nOVERVIEW=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$TOPIC overview\\\"}\")\nSTATISTICS=$(infsh app run exa\u002Fsearch --input \"{\\\"query\\\": \\\"$TOPIC statistics data\\\"}\")\nOPINIONS=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$TOPIC expert opinions\\\"}\")\n\n# 2. Generate comprehensive report\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Generate a comprehensive research report on: $TOPIC\n\nResearch Data:\n== Overview ==\n$OVERVIEW\n\n== Statistics ==\n$STATISTICS\n\n== Expert Opinions ==\n$OPINIONS\n\nFormat as a professional report with:\n- Executive Summary\n- Key Findings\n- Data Analysis\n- Expert Perspectives\n- Conclusion\n- Sources\\\"\n}\"\n```\n\n### Quick Answer with Sources\n\n```bash\n# Use Exa Answer for direct factual questions\ninfsh app run exa\u002Fanswer --input '{\n  \"question\": \"What is the current market cap of NVIDIA?\"\n}'\n```\n\n## Best Practices\n\n### 1. Query Optimization\n\n```bash\n# Bad: Too vague\n\"AI news\"\n\n# Good: Specific and contextual\n\"latest developments in large language models January 2024\"\n```\n\n### 2. Context Management\n\n```bash\n# Summarize long search results before sending to LLM\nSEARCH=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"...\"}')\n\n# If too long, summarize first\nSUMMARY=$(infsh app run openrouter\u002Fclaude-haiku-45 --input \"{\n  \\\"prompt\\\": \\\"Summarize these search results in bullet points: $SEARCH\\\"\n}\")\n\n# Then use summary for analysis\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input \"{\n  \\\"prompt\\\": \\\"Based on this research summary, provide insights: $SUMMARY\\\"\n}\"\n```\n\n### 3. Source Attribution\n\nAlways ask the LLM to cite sources:\n\n```bash\ninfsh app run openrouter\u002Fclaude-sonnet-45 --input '{\n  \"prompt\": \"... Always cite sources in [Source Name](URL) format.\"\n}'\n```\n\n### 4. Iterative Research\n\n```bash\n# First pass: broad search\nINITIAL=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"topic overview\"}')\n\n# Second pass: dive deeper based on findings\nDEEP=$(infsh app run tavily\u002Fsearch-assistant --input '{\"query\": \"specific aspect from initial search\"}')\n```\n\n## Pipeline Templates\n\n### Agent Research Tool\n\n```bash\n#!\u002Fbin\u002Fbash\n# research.sh - Reusable research function\n\nresearch() {\n  local query=\"$1\"\n\n  # Search\n  local results=$(infsh app run tavily\u002Fsearch-assistant --input \"{\\\"query\\\": \\\"$query\\\"}\")\n\n  # Analyze\n  infsh app run openrouter\u002Fclaude-haiku-45 --input \"{\n    \\\"prompt\\\": \\\"Summarize: $results\\\"\n  }\"\n}\n\nresearch \"your query here\"\n```\n\n## Related Skills\n\n```bash\n# Web search tools\nnpx skills add inference-sh\u002Fagent-skills@web-search\n\n# LLM models\nnpx skills add inference-sh\u002Fagent-skills@llm-models\n\n# Content pipelines\nnpx skills add inference-sh\u002Fagent-skills@ai-content-pipeline\n\n# Full platform skill\nnpx skills add inference-sh\u002Fagent-skills@inference-sh\n```\n\nBrowse all apps: `infsh app list`\n\n## Documentation\n\n- [Adding Tools to Agents](https:\u002F\u002Finference.sh\u002Fdocs\u002Fagents\u002Fadding-tools) - Agent tool integration\n- [Building a Research Agent](https:\u002F\u002Finference.sh\u002Fblog\u002Fguides\u002Fresearch-agent) - Full guide\n",{"name":5,"description":48,"allowed-tools":49},"Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.\nTools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter.\nCapabilities: research, fact-checking, grounded responses, knowledge retrieval.\nUse for: AI agents, research assistants, fact-checkers, knowledge bases.\nTriggers: rag, retrieval augmented generation, grounded ai, search and answer,\nresearch agent, fact checking, knowledge retrieval, ai research, search + llm,\nweb grounded, perplexity alternative, ai with sources, citation, research pipeline\n","Bash(infsh *)","main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fai-rag-pipeline",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill ai-rag-pipeline",{"tool":64,"command":65},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill ai-rag-pipeline",{"tool":67,"command":68},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill ai-rag-pipeline","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fai-rag-pipeline\u002FSKILL.md",true,[72],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"_meta.json","REFERENCE","参考文档",282,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fai-rag-pipeline\u002F_meta.json",[79,103,137,148,158,188],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":43},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[81,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":104,"name":105,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":119,"publishedAt":43},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[108,111,114],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[120,121,122,123,124,125,126,88,127,92,128,129,130,131,132,133,134,135,136],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":138,"name":139,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":145,"publishedAt":43},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[142,143,144],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[120,146,147,121,122,123,124,125,126,88,127,92,129,130,131,132,133,134,135,136],"api skill","zerion api skill",{"skillNo":149,"name":150,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":156,"publishedAt":43},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[153,154,155],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[123,120,157,121,122,124,125,88,127,92,128,129,130,131,132,133,134,135,136],"zerion api mcp",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":167,"publishedAt":43},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[164],{"categoryNo":165,"categoryName":166,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000040","财务",[168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data","for",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":43},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[194],{"categoryNo":165,"categoryName":166,"taxonomyType":117,"taxonomyTypeDesc":118},[168,169,171,196,197,198,199,200,201,179,202,203,204,205,206,207,208,209,136,186],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]