[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FK0YV23VDPG8QAMCBQQ2KK":3,"skill-same:01M6FK0YV23VDPG8QAMCBQQ2KK":60},{"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":17,"publishedAt":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":39,"install":40,"resources":53},"01M6FK0YV23VDPG8QAMCBQQ2KK","n8n-cost-estimation",null,"Build n8n pipeline for automated cost estimation from Revit\u002FIFC using DDC CWICR database and LLM classification.","kbarbel640-del\u002Fskills","kbarbel640-del","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F255315490?v=4","01M6CJPPQGCCESXMG3K7BBGQ5J","01M6CJPPX2PB2BCZERFVM2C2B3",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fn8n-cost-estimation\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26],"cost estimation","cost","n8n","estimation","n8n cost estimation","database","pipeline","classification","build","2026-11-10 10:12:30",false,"---\r\nslug: \"n8n-cost-estimation\"\r\ndisplay_name: \"N8N Cost Estimation\"\r\ndescription: \"Build n8n pipeline for automated cost estimation from Revit\u002FIFC using DDC CWICR database and LLM classification.\"\r\n---\r\n\r\n# Automated Cost Estimation Pipeline\r\n\r\n## Business Case\r\n\r\n### Problem Statement\r\nTraditional cost estimation requires:\r\n- Manual work item lookup in price databases\r\n- Time-consuming element classification\r\n- Expert knowledge of pricing standards\r\n- Repetitive data entry\r\n\r\n### Solution\r\nFree open-source n8n pipeline that converts CAD (Revit 2015-2026) files into full cost and time estimates using AI (LLM) and vector database with 55,000+ work items.\r\n\r\n### Business Value\r\n\r\n| Traditional Role | Automated Alternative |\r\n|-----------------|----------------------|\r\n| BIM Manager manually exports data | Pipeline auto-classifies elements |\r\n| Junior Estimator searches databases | Vector search finds matches in ms |\r\n| Senior Estimator maps assemblies | LLM identifies quantity parameters |\r\n| Foreman calculates labor hours | DDC CWICR contains documented norms |\r\n| Project Manager aggregates costs | Pipeline outputs phased breakdown |\r\n\r\n**Processing speed**: 3-10 seconds per element group\r\n\r\n## Technical Implementation\r\n\r\n### Pipeline Architecture\r\n```\r\n┌─────────────┐    ┌─────────────┐    ┌─────────────┐\r\n│ Revit\u002FIFC   │───>│ CAD2DATA    │───>│ Structured  │\r\n│ File        │    │ Converter   │    │ Excel\u002FCSV   │\r\n└─────────────┘    └─────────────┘    └─────────────┘\r\n                                             │\r\n                                             ▼\r\n┌─────────────┐    ┌─────────────┐    ┌─────────────┐\r\n│ Cost Report │\u003C───│ Price Match │\u003C───│ LLM Class.  │\r\n│ HTML\u002FExcel  │    │ DDC CWICR   │    │ + QTO       │\r\n└─────────────┘    └─────────────┘    └─────────────┘\r\n```\r\n\r\n### n8n Pipeline Steps\r\n\r\n#### 1. File Conversion Node\r\n```javascript\r\n\u002F\u002F Execute CAD converter\r\nconst filePath = $input.first().json.file_path;\r\nconst outputDir = filePath.replace(\u002F\\.[^.]+$\u002F, '');\r\n\r\nconst command = `RvtExporter.exe \"${filePath}\" complete bbox`;\r\n\r\n\u002F\u002F Returns: { xlsx_path, dae_path }\r\n```\r\n\r\n#### 2. Load Elements\r\n```javascript\r\n\u002F\u002F Read converted Excel into n8n\r\nconst xlsx = $node[\"Read Binary Files\"].json;\r\nconst elements = xlsx.sheets[\"Elements\"];\r\n\r\n\u002F\u002F Group by category for processing\r\nconst grouped = elements.reduce((acc, el) => {\r\n  const cat = el.Category;\r\n  if (!acc[cat]) acc[cat] = [];\r\n  acc[cat].push(el);\r\n  return acc;\r\n}, {});\r\n\r\nreturn Object.entries(grouped).map(([category, items]) => ({\r\n  json: {category, items, count: items.length}\r\n}));\r\n```\r\n\r\n#### 3. LLM Classification\r\n```javascript\r\n\u002F\u002F Prompt for Claude\u002FGPT classification\r\nconst prompt = `\r\nYou are a construction estimator. Given these BIM elements:\r\nCategory: ${$input.first().json.category}\r\nSample elements: ${JSON.stringify($input.first().json.items.slice(0,5))}\r\n\r\n1. Identify the construction work type\r\n2. List relevant quantity parameters (Volume, Area, Length, Count)\r\n3. Suggest standard work items from construction norms\r\n\r\nReturn as JSON:\r\n{\r\n  \"work_type\": \"...\",\r\n  \"quantity_params\": [\"Volume\", \"Area\"],\r\n  \"suggested_items\": [\"Concrete foundation\", \"Formwork\"]\r\n}\r\n`;\r\n```\r\n\r\n#### 4. Vector Search in CWICR\r\n```javascript\r\n\u002F\u002F Search DDC CWICR database for matching work items\r\nconst qdrantClient = require('@qdrant\u002Fjs-client-rest');\r\n\r\nconst searchResults = await qdrantClient.search('ddc_cwicr_en', {\r\n  vector: await getEmbedding($input.first().json.work_description),\r\n  limit: 10,\r\n  score_threshold: 0.7\r\n});\r\n\r\nreturn searchResults.map(r => ({\r\n  json: {\r\n    work_code: r.payload.work_item_code,\r\n    description: r.payload.description,\r\n    unit: r.payload.unit,\r\n    unit_price: r.payload.unit_price,\r\n    similarity: r.score\r\n  }\r\n}));\r\n```\r\n\r\n#### 5. Calculate Costs\r\n```javascript\r\n\u002F\u002F Match quantities to prices\r\nconst elements = $node[\"Load Elements\"].json;\r\nconst prices = $node[\"Vector Search\"].json;\r\n\r\nlet totalCost = 0;\r\nconst breakdown = [];\r\n\r\nfor (const el of elements.items) {\r\n  const matchedPrice = prices.find(p => p.similarity > 0.8);\r\n  if (matchedPrice) {\r\n    const quantity = el.Volume || el.Area || 1;\r\n    const cost = quantity * matchedPrice.unit_price;\r\n    totalCost += cost;\r\n\r\n    breakdown.push({\r\n      element: el.Name,\r\n      quantity: quantity,\r\n      unit: matchedPrice.unit,\r\n      unit_price: matchedPrice.unit_price,\r\n      total: cost\r\n    });\r\n  }\r\n}\r\n\r\nreturn [{json: {totalCost, breakdown}}];\r\n```\r\n\r\n#### 6. Generate Report\r\n```javascript\r\n\u002F\u002F Create HTML report\r\nconst data = $input.first().json;\r\n\r\nconst html = `\r\n\u003C!DOCTYPE html>\r\n\u003Chtml>\r\n\u003Chead>\r\n  \u003Ctitle>Cost Estimate Report\u003C\u002Ftitle>\r\n  \u003Cstyle>\r\n    body { font-family: Arial, sans-serif; margin: 20px; }\r\n    table { border-collapse: collapse; width: 100%; }\r\n    th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }\r\n    th { background-color: #4CAF50; color: white; }\r\n    .total { font-size: 1.5em; font-weight: bold; }\r\n  \u003C\u002Fstyle>\r\n\u003C\u002Fhead>\r\n\u003Cbody>\r\n  \u003Ch1>Cost Estimate Report\u003C\u002Fh1>\r\n  \u003Cp class=\"total\">Total: $${data.totalCost.toLocaleString()}\u003C\u002Fp>\r\n  \u003Ctable>\r\n    \u003Ctr>\u003Cth>Element\u003C\u002Fth>\u003Cth>Quantity\u003C\u002Fth>\u003Cth>Unit\u003C\u002Fth>\u003Cth>Price\u003C\u002Fth>\u003Cth>Total\u003C\u002Fth>\u003C\u002Ftr>\r\n    ${data.breakdown.map(row => `\r\n      \u003Ctr>\r\n        \u003Ctd>${row.element}\u003C\u002Ftd>\r\n        \u003Ctd>${row.quantity.toFixed(2)}\u003C\u002Ftd>\r\n        \u003Ctd>${row.unit}\u003C\u002Ftd>\r\n        \u003Ctd>$${row.unit_price.toFixed(2)}\u003C\u002Ftd>\r\n        \u003Ctd>$${row.total.toFixed(2)}\u003C\u002Ftd>\r\n      \u003C\u002Ftr>\r\n    `).join('')}\r\n  \u003C\u002Ftable>\r\n\u003C\u002Fbody>\r\n\u003C\u002Fhtml>\r\n`;\r\n\r\nreturn [{json: {html, filename: 'estimate_report.html'}}];\r\n```\r\n\r\n## Real-World Results\r\n\r\nExample project (rac_basic_sample.rvt):\r\n- Processing time: ~30 minutes with ChatGPT\r\n- Elements analyzed: 500+\r\n- Automatic classification: 95% accuracy\r\n- Manual review needed: 5% edge cases\r\n\r\n## Key Insight from Community\r\n\r\n> \"My subjective take: professionals who ignore workflow automation and AI-agents today have roughly 5 years before the construction industry moves past them. The tools are free and open. The data is open. The only question is who learns to use them first.\"\r\n\r\n## Prerequisites\r\n\r\n- n8n (local or hosted)\r\n- DDC CAD converters\r\n- DDC CWICR database\r\n- OpenAI\u002FAnthropic API key\r\n- Qdrant vector database\r\n\r\n## Resources\r\n\r\n- **GitHub**: cad2data Pipeline repository\r\n- **Database**: OpenConstructionEstimate-DDC-CWICR\r\n- **Community**: n8n Workflows for Construction (Telegram)\r\n","# Automated Cost Estimation Pipeline\r\n\r\n## Business Case\r\n\r\n### Problem Statement\r\nTraditional cost estimation requires:\r\n- Manual work item lookup in price databases\r\n- Time-consuming element classification\r\n- Expert knowledge of pricing standards\r\n- Repetitive data entry\r\n\r\n### Solution\r\nFree open-source n8n pipeline that converts CAD (Revit 2015-2026) files into full cost and time estimates using AI (LLM) and vector database with 55,000+ work items.\r\n\r\n### Business Value\r\n\r\n| Traditional Role | Automated Alternative |\r\n|-----------------|----------------------|\r\n| BIM Manager manually exports data | Pipeline auto-classifies elements |\r\n| Junior Estimator searches databases | Vector search finds matches in ms |\r\n| Senior Estimator maps assemblies | LLM identifies quantity parameters |\r\n| Foreman calculates labor hours | DDC CWICR contains documented norms |\r\n| Project Manager aggregates costs | Pipeline outputs phased breakdown |\r\n\r\n**Processing speed**: 3-10 seconds per element group\r\n\r\n## Technical Implementation\r\n\r\n### Pipeline Architecture\r\n```\r\n┌─────────────┐    ┌─────────────┐    ┌─────────────┐\r\n│ Revit\u002FIFC   │───>│ CAD2DATA    │───>│ Structured  │\r\n│ File        │    │ Converter   │    │ Excel\u002FCSV   │\r\n└─────────────┘    └─────────────┘    └─────────────┘\r\n                                             │\r\n                                             ▼\r\n┌─────────────┐    ┌─────────────┐    ┌─────────────┐\r\n│ Cost Report │\u003C───│ Price Match │\u003C───│ LLM Class.  │\r\n│ HTML\u002FExcel  │    │ DDC CWICR   │    │ + QTO       │\r\n└─────────────┘    └─────────────┘    └─────────────┘\r\n```\r\n\r\n### n8n Pipeline Steps\r\n\r\n#### 1. File Conversion Node\r\n```javascript\r\n\u002F\u002F Execute CAD converter\r\nconst filePath = $input.first().json.file_path;\r\nconst outputDir = filePath.replace(\u002F\\.[^.]+$\u002F, '');\r\n\r\nconst command = `RvtExporter.exe \"${filePath}\" complete bbox`;\r\n\r\n\u002F\u002F Returns: { xlsx_path, dae_path }\r\n```\r\n\r\n#### 2. Load Elements\r\n```javascript\r\n\u002F\u002F Read converted Excel into n8n\r\nconst xlsx = $node[\"Read Binary Files\"].json;\r\nconst elements = xlsx.sheets[\"Elements\"];\r\n\r\n\u002F\u002F Group by category for processing\r\nconst grouped = elements.reduce((acc, el) => {\r\n  const cat = el.Category;\r\n  if (!acc[cat]) acc[cat] = [];\r\n  acc[cat].push(el);\r\n  return acc;\r\n}, {});\r\n\r\nreturn Object.entries(grouped).map(([category, items]) => ({\r\n  json: {category, items, count: items.length}\r\n}));\r\n```\r\n\r\n#### 3. LLM Classification\r\n```javascript\r\n\u002F\u002F Prompt for Claude\u002FGPT classification\r\nconst prompt = `\r\nYou are a construction estimator. Given these BIM elements:\r\nCategory: ${$input.first().json.category}\r\nSample elements: ${JSON.stringify($input.first().json.items.slice(0,5))}\r\n\r\n1. Identify the construction work type\r\n2. List relevant quantity parameters (Volume, Area, Length, Count)\r\n3. Suggest standard work items from construction norms\r\n\r\nReturn as JSON:\r\n{\r\n  \"work_type\": \"...\",\r\n  \"quantity_params\": [\"Volume\", \"Area\"],\r\n  \"suggested_items\": [\"Concrete foundation\", \"Formwork\"]\r\n}\r\n`;\r\n```\r\n\r\n#### 4. Vector Search in CWICR\r\n```javascript\r\n\u002F\u002F Search DDC CWICR database for matching work items\r\nconst qdrantClient = require('@qdrant\u002Fjs-client-rest');\r\n\r\nconst searchResults = await qdrantClient.search('ddc_cwicr_en', {\r\n  vector: await getEmbedding($input.first().json.work_description),\r\n  limit: 10,\r\n  score_threshold: 0.7\r\n});\r\n\r\nreturn searchResults.map(r => ({\r\n  json: {\r\n    work_code: r.payload.work_item_code,\r\n    description: r.payload.description,\r\n    unit: r.payload.unit,\r\n    unit_price: r.payload.unit_price,\r\n    similarity: r.score\r\n  }\r\n}));\r\n```\r\n\r\n#### 5. Calculate Costs\r\n```javascript\r\n\u002F\u002F Match quantities to prices\r\nconst elements = $node[\"Load Elements\"].json;\r\nconst prices = $node[\"Vector Search\"].json;\r\n\r\nlet totalCost = 0;\r\nconst breakdown = [];\r\n\r\nfor (const el of elements.items) {\r\n  const matchedPrice = prices.find(p => p.similarity > 0.8);\r\n  if (matchedPrice) {\r\n    const quantity = el.Volume || el.Area || 1;\r\n    const cost = quantity * matchedPrice.unit_price;\r\n    totalCost += cost;\r\n\r\n    breakdown.push({\r\n      element: el.Name,\r\n      quantity: quantity,\r\n      unit: matchedPrice.unit,\r\n      unit_price: matchedPrice.unit_price,\r\n      total: cost\r\n    });\r\n  }\r\n}\r\n\r\nreturn [{json: {totalCost, breakdown}}];\r\n```\r\n\r\n#### 6. Generate Report\r\n```javascript\r\n\u002F\u002F Create HTML report\r\nconst data = $input.first().json;\r\n\r\nconst html = `\r\n\u003C!DOCTYPE html>\r\n\u003Chtml>\r\n\u003Chead>\r\n  \u003Ctitle>Cost Estimate Report\u003C\u002Ftitle>\r\n  \u003Cstyle>\r\n    body { font-family: Arial, sans-serif; margin: 20px; }\r\n    table { border-collapse: collapse; width: 100%; }\r\n    th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }\r\n    th { background-color: #4CAF50; color: white; }\r\n    .total { font-size: 1.5em; font-weight: bold; }\r\n  \u003C\u002Fstyle>\r\n\u003C\u002Fhead>\r\n\u003Cbody>\r\n  \u003Ch1>Cost Estimate Report\u003C\u002Fh1>\r\n  \u003Cp class=\"total\">Total: $${data.totalCost.toLocaleString()}\u003C\u002Fp>\r\n  \u003Ctable>\r\n    \u003Ctr>\u003Cth>Element\u003C\u002Fth>\u003Cth>Quantity\u003C\u002Fth>\u003Cth>Unit\u003C\u002Fth>\u003Cth>Price\u003C\u002Fth>\u003Cth>Total\u003C\u002Fth>\u003C\u002Ftr>\r\n    ${data.breakdown.map(row => `\r\n      \u003Ctr>\r\n        \u003Ctd>${row.element}\u003C\u002Ftd>\r\n        \u003Ctd>${row.quantity.toFixed(2)}\u003C\u002Ftd>\r\n        \u003Ctd>${row.unit}\u003C\u002Ftd>\r\n        \u003Ctd>$${row.unit_price.toFixed(2)}\u003C\u002Ftd>\r\n        \u003Ctd>$${row.total.toFixed(2)}\u003C\u002Ftd>\r\n      \u003C\u002Ftr>\r\n    `).join('')}\r\n  \u003C\u002Ftable>\r\n\u003C\u002Fbody>\r\n\u003C\u002Fhtml>\r\n`;\r\n\r\nreturn [{json: {html, filename: 'estimate_report.html'}}];\r\n```\r\n\r\n## Real-World Results\r\n\r\nExample project (rac_basic_sample.rvt):\r\n- Processing time: ~30 minutes with ChatGPT\r\n- Elements analyzed: 500+\r\n- Automatic classification: 95% accuracy\r\n- Manual review needed: 5% edge cases\r\n\r\n## Key Insight from Community\r\n\r\n> \"My subjective take: professionals who ignore workflow automation and AI-agents today have roughly 5 years before the construction industry moves past them. The tools are free and open. The data is open. The only question is who learns to use them first.\"\r\n\r\n## Prerequisites\r\n\r\n- n8n (local or hosted)\r\n- DDC CAD converters\r\n- DDC CWICR database\r\n- OpenAI\u002FAnthropic API key\r\n- Qdrant vector database\r\n\r\n## Resources\r\n\r\n- **GitHub**: cad2data Pipeline repository\r\n- **Database**: OpenConstructionEstimate-DDC-CWICR\r\n- **Community**: n8n Workflows for Construction (Telegram)\r\n",{"slug":5,"description":7,"display_name":32},"N8N Cost Estimation","main","e7e336ac628c3e3bd771aff1aaa469c93778f94a","skills\u002Fdatadrivenconstruction\u002Fn8n-cost-estimation",0,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del","2026-08-23 22:39:37",{"source":8,"skillName":5,"commands":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add kbarbel640-del\u002Fskills --skill n8n-cost-estimation",{"tool":46,"command":47},"bunx","bunx skills add kbarbel640-del\u002Fskills --skill n8n-cost-estimation",{"tool":49,"command":50},"pnpm","pnpm dlx skills add kbarbel640-del\u002Fskills --skill n8n-cost-estimation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fn8n-cost-estimation\u002FSKILL.md",true,[54],{"path":55,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":58,"sourceHtmlUrl":59},"_meta.json","REFERENCE","参考文档",306,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fn8n-cost-estimation\u002F_meta.json",[61,92,121,142,148,168],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":72,"publishedAt":27},"01M6G8J55967D6281ACMKAKTEQ","crowd-prompting","A marketplace where AI agents improve prompts, system instructions, tool descriptions, and other text-based content with domain expertise from real-world operations — and earn tokens for valuable contributions.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzzadrian\u002Fcrowd-prompting\u002FSKILL.md",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91],"crowd prompting","prompting","api","with","tokens","domain","world","prompts","operations","instructions","text","tool","system","improve","productivity","marketplace","https","content","ai agents",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":27},"01M6G8J0PKPAYVET6BWE8W4C26","dilemm-ai","Compete in DilemmAI, the prisoner's dilemma AI arena at dilemm.ai. Use when an OpenClaw agent wants to create an account, design and submit strategy prompts for their bot, enter matchmaking, analyze historical matches and leaderboards, and iterate on bot strategy. Requires headless browser automation (no API available yet).","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzyori\u002Fdilemm-ai\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[104,75,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,80,120],"dilemm ai","arena","analyze","browser","openclaw agent","create","automation","browser automation","bot","openclaw","design","strategy","headless","compete","submit","matchmaking","use",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":130,"publishedAt":27},"01M6G8HWAF3K9SW7NP3Z83CPJ6","arxiv-paper-reviews","Interact with arXiv Crawler API to fetch papers, read reviews, and submit comments. Use when working with arXiv papers, fetching paper lists by date\u002Fcategory\u002Finterest, viewing paper details with comments, or submitting paper reviews via API at http:\u002F\u002F150.158.152.82:8000.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Fweak-accept\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000021","资料检索与研究",[131,132,133,134,135,136,137,76,138,118,120,139,75,140,141],"arxiv paper reviews","reviews","arxiv","paper","crawler","papers","comments","fetch","read","http","via",{"skillNo":143,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":27},"01M6G8HQJ7N7WMY6YV7D7SSJ16","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Farxiv-paper-reviews\u002FSKILL.md",[146],{"categoryNo":128,"categoryName":129,"taxonomyType":101,"taxonomyTypeDesc":102},[134,133,132,131,141,136,137,76,135,118,120,138,139,75,140],{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":27},"01M6G8HM95Y64JNVFC2HN4E2BG","git-summary","Get a quick summary of the current Git repository including status, recent commits, branches, and contributors.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzweack\u002Fgit-summary\u002FSKILL.md",[],[155,156,157,158,159,160,161,162,163,164,165,166,167],"git","summary","git summary","commits","repository","win32","status","linux","contributors","true","quick","get","branches",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":178,"publishedAt":27},"01M6G8HGEGF05CNEJCGVYFSC4R","agent-constitution","Interact with AgentConstitution governance contracts on Base Sepolia. Check compliance, read rules, log actions, query governance state.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fztsalexey\u002Fagent-constitution\u002FSKILL.md",[174,175],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":176,"categoryName":177,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000037","律师与法务",[179,180,181,182,76,183,184,33,185,139,186,187,188,189,190,89,191,192,193],"agent constitution","constitution","contracts","tree","query","base","log","check","Rules","projects","compliance","actions","state","governance","github"]