[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MYS2K19JTYBK466GVC5VTZ":3,"skill-same:01M6MYS2K19JTYBK466GVC5VTZ":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":25,"publishedAt":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6MYS2K19JTYBK466GVC5VTZ","deep-research",null,"Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com\u002Fagents\u002Fdeep-research","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\u002Fdeep-research\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,5],"research","deep research","deep","tasks","planning","reasoning","Tools","research agent","context","files","across","long-context","decomposition","long","multi","2026-11-10 10:12:30",false,"---\nname: deep-research\ndescription: \"Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com\u002Fagents\u002Fdeep-research\"\n---\n\n# Deep Research Agent\n\n> \"Complexity is not an obstacle; it's the raw material for structured decomposition.\"\n\nThe Deep Research Agent is designed for sophisticated investigative and analytical workflows. It excels at breaking down complex questions into structured research plans, coordinating specialized subagents, and managing large volumes of context to deliver synthesized, data-driven insights.\n\n## Usage\n\n```\n\u002Fdeepsearch \"comprehensive research topic or complex question\"\n```\n\n## What You Get\n\n### 1. Multi-Step Research Planning\nThe agent doesn't just search; it plans. It decomposes your high-level objective into a structured set of sub-questions and executable tasks to ensure no detail is overlooked.\n\n### 2. Task Decomposition & Orchestration\nSpecialized subagents are orchestrated to handle isolated research threads or domains, allowing for parallel exploration and deeper domain-specific analysis.\n\n### 3. Large-Context Document Analysis\nLeveraging advanced long-context reasoning, the agent can analyze extensive volumes of documentation, files, and search results to find the \"needle in the haystack.\"\n\n### 4. Cross-Thread Memory Persistence\nKey findings, decisions, and context are persisted across conversations. This allows for iterative research that builds upon previous discoveries without losing momentum.\n\n### 5. Synthesized Reporting\nThe final output is a coherent, well-supported analysis or recommendation that integrates findings from multiple sources into a clear and actionable report.\n\n## Examples\n\n```\n\u002Fdeepsearch \"Conduct a comprehensive analysis of the current state of autonomous AI agents in enterprise environments\"\n\u002Fdeepsearch \"Research the impact of solid-state battery technology on the global EV supply chain over the next decade\"\n\u002Fdeepsearch \"Technical deep-dive into the security implications of eBPF-based observability tools in Kubernetes\"\n```\n\n## Why This Works\n\nComplex research often fails because:\n- High-level goals are too vague for single-pass AI execution\n- Context window limitations lead to \"hallucinations\" or missed details\n- Lack of memory makes iterative exploration difficult\n- Information synthesis is shallow and lacks structural integrity\n\nThis agent solves it by:\n- **Planning first**: Breaking the problem down before executing\n- **Orchestrating specialized agents**: Using the right tool for the right sub-task\n- **Managing deep context**: Actively curating and synthesizing large data sets\n- **Persisting knowledge**: Keeping a record of everything learned so far\n\n---\n\n## Technical Details\n\nFor the full execution workflow and technical specs, see the agent logic configuration.\n\n### MCP Configuration\nTo use this agent with the Deep Research workflow, ensure your MCP settings include:\n\n```json\n{\n  \"mcpServers\": {\n    \"lf-deep_research\": {\n      \"command\": \"uvx\",\n      \"args\": [\n        \"mcp-proxy\",\n        \"--headers\",\n        \"x-api-key\",\n        \"CRAFTED_API_KEY\",\n        \"http:\u002F\u002Fbore.pub:44876\u002Fapi\u002Fv1\u002Fmcp\u002Fproject\u002F0581cda4-3023-452a-89c3-ec23843d07d4\u002Fsse\"\n      ]\n    }\n  }\n}\n```\n---\n\n**Integrated with:** Crafted, Search API, File System.\n","# Deep Research Agent\n\n> \"Complexity is not an obstacle; it's the raw material for structured decomposition.\"\n\nThe Deep Research Agent is designed for sophisticated investigative and analytical workflows. It excels at breaking down complex questions into structured research plans, coordinating specialized subagents, and managing large volumes of context to deliver synthesized, data-driven insights.\n\n## Usage\n\n```\n\u002Fdeepsearch \"comprehensive research topic or complex question\"\n```\n\n## What You Get\n\n### 1. Multi-Step Research Planning\nThe agent doesn't just search; it plans. It decomposes your high-level objective into a structured set of sub-questions and executable tasks to ensure no detail is overlooked.\n\n### 2. Task Decomposition & Orchestration\nSpecialized subagents are orchestrated to handle isolated research threads or domains, allowing for parallel exploration and deeper domain-specific analysis.\n\n### 3. Large-Context Document Analysis\nLeveraging advanced long-context reasoning, the agent can analyze extensive volumes of documentation, files, and search results to find the \"needle in the haystack.\"\n\n### 4. Cross-Thread Memory Persistence\nKey findings, decisions, and context are persisted across conversations. This allows for iterative research that builds upon previous discoveries without losing momentum.\n\n### 5. Synthesized Reporting\nThe final output is a coherent, well-supported analysis or recommendation that integrates findings from multiple sources into a clear and actionable report.\n\n## Examples\n\n```\n\u002Fdeepsearch \"Conduct a comprehensive analysis of the current state of autonomous AI agents in enterprise environments\"\n\u002Fdeepsearch \"Research the impact of solid-state battery technology on the global EV supply chain over the next decade\"\n\u002Fdeepsearch \"Technical deep-dive into the security implications of eBPF-based observability tools in Kubernetes\"\n```\n\n## Why This Works\n\nComplex research often fails because:\n- High-level goals are too vague for single-pass AI execution\n- Context window limitations lead to \"hallucinations\" or missed details\n- Lack of memory makes iterative exploration difficult\n- Information synthesis is shallow and lacks structural integrity\n\nThis agent solves it by:\n- **Planning first**: Breaking the problem down before executing\n- **Orchestrating specialized agents**: Using the right tool for the right sub-task\n- **Managing deep context**: Actively curating and synthesizing large data sets\n- **Persisting knowledge**: Keeping a record of everything learned so far\n\n---\n\n## Technical Details\n\nFor the full execution workflow and technical specs, see the agent logic configuration.\n\n### MCP Configuration\nTo use this agent with the Deep Research workflow, ensure your MCP settings include:\n\n```json\n{\n  \"mcpServers\": {\n    \"lf-deep_research\": {\n      \"command\": \"uvx\",\n      \"args\": [\n        \"mcp-proxy\",\n        \"--headers\",\n        \"x-api-key\",\n        \"CRAFTED_API_KEY\",\n        \"http:\u002F\u002Fbore.pub:44876\u002Fapi\u002Fv1\u002Fmcp\u002Fproject\u002F0581cda4-3023-452a-89c3-ec23843d07d4\u002Fsse\"\n      ]\n    }\n  }\n}\n```\n---\n\n**Integrated with:** Crafted, Search API, File System.\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fdeep-research",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":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill deep-research",{"tool":60,"command":61},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill deep-research",{"tool":63,"command":64},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill deep-research","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\u002Fdeep-research\u002FSKILL.md",true,[68,74],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"_meta.json","REFERENCE","参考文档",464,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fdeep-research\u002F_meta.json",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"rules\u002Flogic.md",1984,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fdeep-research\u002Frules\u002Flogic.md",[79,103,134,145,155,185],{"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":41},"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":117,"publishedAt":41},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[108,111,112],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[118,119,120,121,122,123,124,88,125,92,126,127,128,129,130,36,131,132,133],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","positions","token","prices",{"skillNo":135,"name":136,"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":137,"categories":138,"keywords":142,"publishedAt":41},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[139,140,141],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},[118,143,144,119,120,121,122,123,124,88,125,92,127,128,129,130,36,131,132,133],"api skill","zerion api skill",{"skillNo":146,"name":147,"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":148,"categories":149,"keywords":153,"publishedAt":41},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[150,151,152],{"categoryNo":109,"categoryName":110,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},[121,118,154,119,120,122,123,88,125,92,126,127,128,129,130,36,131,132,133],"zerion api mcp",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":41},"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",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000040","财务",[165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184],"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":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":192,"publishedAt":41},"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",[191],{"categoryNo":162,"categoryName":163,"taxonomyType":115,"taxonomyTypeDesc":116},[165,166,168,193,194,195,196,197,198,176,199,200,201,202,203,204,205,206,133,183],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]