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Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI\u002FCD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.","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\u002Falirezarezvani\u002Faws-solution-architect\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"aws solution architect","aws","architect","lambda","optimize","cost","up","iac","use","migrate","api","design","SET","templates","Optimization","ci","gateway","create","cost optimization","architecture","2026-11-10 10:12:30",false,"---\nname: aws-solution-architect\ndescription: Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI\u002FCD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.\n---\n\n# AWS Solution Architect\n\nDesign scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.\n\n---\n\n## Table of Contents\n\n- [Trigger Terms](#trigger-terms)\n- [Workflow](#workflow)\n- [Tools](#tools)\n- [Quick Start](#quick-start)\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n\n---\n\n## Trigger Terms\n\nUse this skill when you encounter:\n\n| Category | Terms |\n|----------|-------|\n| **Architecture Design** | serverless architecture, AWS architecture, cloud design, microservices, three-tier |\n| **IaC Generation** | CloudFormation, CDK, Terraform, infrastructure as code, deploy template |\n| **Serverless** | Lambda, API Gateway, DynamoDB, Step Functions, EventBridge, AppSync |\n| **Containers** | ECS, Fargate, EKS, container orchestration, Docker on AWS |\n| **Cost Optimization** | reduce AWS costs, optimize spending, right-sizing, Savings Plans |\n| **Database** | Aurora, RDS, DynamoDB design, database migration, data modeling |\n| **Security** | IAM policies, VPC design, encryption, Cognito, WAF |\n| **CI\u002FCD** | CodePipeline, CodeBuild, CodeDeploy, GitHub Actions AWS |\n| **Monitoring** | CloudWatch, X-Ray, observability, alarms, dashboards |\n| **Migration** | migrate to AWS, lift and shift, replatform, DMS |\n\n---\n\n## Workflow\n\n### Step 1: Gather Requirements\n\nCollect application specifications:\n\n```\n- Application type (web app, mobile backend, data pipeline, SaaS)\n- Expected users and requests per second\n- Budget constraints (monthly spend limit)\n- Team size and AWS experience level\n- Compliance requirements (GDPR, HIPAA, SOC 2)\n- Availability requirements (SLA, RPO\u002FRTO)\n```\n\n### Step 2: Design Architecture\n\nRun the architecture designer to get pattern recommendations:\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json\n```\n\nSelect from recommended patterns:\n- **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB\n- **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions\n- **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache\n- **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito\n\nSee `references\u002Farchitecture_patterns.md` for detailed pattern specifications.\n\n### Step 3: Generate IaC Templates\n\nCreate infrastructure-as-code for the selected pattern:\n\n```bash\n# Serverless stack (CloudFormation)\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n\n# Output: CloudFormation YAML template ready to deploy\n```\n\n### Step 4: Review Costs\n\nAnalyze estimated costs and optimization opportunities:\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources current_setup.json --monthly-spend 2000\n```\n\nOutput includes:\n- Monthly cost breakdown by service\n- Right-sizing recommendations\n- Savings Plans opportunities\n- Potential monthly savings\n\n### Step 5: Deploy\n\nDeploy the generated infrastructure:\n\n```bash\n# CloudFormation\naws cloudformation create-stack \\\n  --stack-name my-app-stack \\\n  --template-body file:\u002F\u002Ftemplate.yaml \\\n  --capabilities CAPABILITY_IAM\n\n# CDK\ncdk deploy\n\n# Terraform\nterraform init && terraform apply\n```\n\n### Step 6: Validate\n\nVerify deployment and set up monitoring:\n\n```bash\n# Check stack status\naws cloudformation describe-stacks --stack-name my-app-stack\n\n# Set up CloudWatch alarms\naws cloudwatch put-metric-alarm --alarm-name high-errors ...\n```\n\n---\n\n## Tools\n\n### architecture_designer.py\n\nGenerates architecture patterns based on requirements.\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json --output design.json\n```\n\n**Input:** JSON with app type, scale, budget, compliance needs\n**Output:** Recommended pattern, service stack, cost estimate, pros\u002Fcons\n\n### serverless_stack.py\n\nCreates serverless CloudFormation templates.\n\n```bash\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Output:** Production-ready CloudFormation YAML with:\n- API Gateway + Lambda\n- DynamoDB table\n- Cognito user pool\n- IAM roles with least privilege\n- CloudWatch logging\n\n### cost_optimizer.py\n\nAnalyzes costs and recommends optimizations.\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources inventory.json --monthly-spend 5000\n```\n\n**Output:** Recommendations for:\n- Idle resource removal\n- Instance right-sizing\n- Reserved capacity purchases\n- Storage tier transitions\n- NAT Gateway alternatives\n\n---\n\n## Quick Start\n\n### MVP Architecture (\u003C $100\u002Fmonth)\n\n```\nAsk: \"Design a serverless MVP backend for a mobile app with 1000 users\"\n\nResult:\n- Lambda + API Gateway for API\n- DynamoDB pay-per-request for data\n- Cognito for authentication\n- S3 + CloudFront for static assets\n- Estimated: $20-50\u002Fmonth\n```\n\n### Scaling Architecture ($500-2000\u002Fmonth)\n\n```\nAsk: \"Design a scalable architecture for a SaaS platform with 50k users\"\n\nResult:\n- ECS Fargate for containerized API\n- Aurora Serverless for relational data\n- ElastiCache for session caching\n- CloudFront for CDN\n- CodePipeline for CI\u002FCD\n- Multi-AZ deployment\n```\n\n### Cost Optimization\n\n```\nAsk: \"Optimize my AWS setup to reduce costs by 30%. Current spend: $3000\u002Fmonth\"\n\nProvide: Current resource inventory (EC2, RDS, S3, etc.)\n\nResult:\n- Idle resource identification\n- Right-sizing recommendations\n- Savings Plans analysis\n- Storage lifecycle policies\n- Target savings: $900\u002Fmonth\n```\n\n### IaC Generation\n\n```\nAsk: \"Generate CloudFormation for a three-tier web app with auto-scaling\"\n\nResult:\n- VPC with public\u002Fprivate subnets\n- ALB with HTTPS\n- ECS Fargate with auto-scaling\n- Aurora with read replicas\n- Security groups and IAM roles\n```\n\n---\n\n## Input Requirements\n\nProvide these details for architecture design:\n\n| Requirement | Description | Example |\n|-------------|-------------|---------|\n| Application type | What you're building | SaaS platform, mobile backend |\n| Expected scale | Users, requests\u002Fsec | 10k users, 100 RPS |\n| Budget | Monthly AWS limit | $500\u002Fmonth max |\n| Team context | Size, AWS experience | 3 devs, intermediate |\n| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |\n| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |\n\n**JSON Format:**\n\n```json\n{\n  \"application_type\": \"saas_platform\",\n  \"expected_users\": 10000,\n  \"requests_per_second\": 100,\n  \"budget_monthly_usd\": 500,\n  \"team_size\": 3,\n  \"aws_experience\": \"intermediate\",\n  \"compliance\": [\"SOC2\"],\n  \"availability_sla\": \"99.9%\"\n}\n```\n\n---\n\n## Output Formats\n\n### Architecture Design\n\n- Pattern recommendation with rationale\n- Service stack diagram (ASCII)\n- Configuration specifications\n- Monthly cost estimate\n- Scaling characteristics\n- Trade-offs and limitations\n\n### IaC Templates\n\n- **CloudFormation YAML**: Production-ready SAM\u002FCFN templates\n- **CDK TypeScript**: Type-safe infrastructure code\n- **Terraform HCL**: Multi-cloud compatible configs\n\n### Cost Analysis\n\n- Current spend breakdown\n- Optimization recommendations with savings\n- Priority action list (high\u002Fmedium\u002Flow)\n- Implementation checklist\n\n---\n\n## Reference Documentation\n\n| Document | Contents |\n|----------|----------|\n| `references\u002Farchitecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region |\n| `references\u002Fservice_selection.md` | Decision matrices for compute, database, storage, messaging |\n| `references\u002Fbest_practices.md` | Serverless design, cost optimization, security hardening, scalability |\n\n---\n\n## Limitations\n\n- Lambda: 15-minute execution, 10GB memory max\n- API Gateway: 29-second timeout, 10MB payload\n- DynamoDB: 400KB item size, eventually consistent by default\n- Regional availability varies by service\n- Some services have AWS-specific lock-in\n","# AWS Solution Architect\n\nDesign scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.\n\n---\n\n## Table of Contents\n\n- [Trigger Terms](#trigger-terms)\n- [Workflow](#workflow)\n- [Tools](#tools)\n- [Quick Start](#quick-start)\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n\n---\n\n## Trigger Terms\n\nUse this skill when you encounter:\n\n| Category | Terms |\n|----------|-------|\n| **Architecture Design** | serverless architecture, AWS architecture, cloud design, microservices, three-tier |\n| **IaC Generation** | CloudFormation, CDK, Terraform, infrastructure as code, deploy template |\n| **Serverless** | Lambda, API Gateway, DynamoDB, Step Functions, EventBridge, AppSync |\n| **Containers** | ECS, Fargate, EKS, container orchestration, Docker on AWS |\n| **Cost Optimization** | reduce AWS costs, optimize spending, right-sizing, Savings Plans |\n| **Database** | Aurora, RDS, DynamoDB design, database migration, data modeling |\n| **Security** | IAM policies, VPC design, encryption, Cognito, WAF |\n| **CI\u002FCD** | CodePipeline, CodeBuild, CodeDeploy, GitHub Actions AWS |\n| **Monitoring** | CloudWatch, X-Ray, observability, alarms, dashboards |\n| **Migration** | migrate to AWS, lift and shift, replatform, DMS |\n\n---\n\n## Workflow\n\n### Step 1: Gather Requirements\n\nCollect application specifications:\n\n```\n- Application type (web app, mobile backend, data pipeline, SaaS)\n- Expected users and requests per second\n- Budget constraints (monthly spend limit)\n- Team size and AWS experience level\n- Compliance requirements (GDPR, HIPAA, SOC 2)\n- Availability requirements (SLA, RPO\u002FRTO)\n```\n\n### Step 2: Design Architecture\n\nRun the architecture designer to get pattern recommendations:\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json\n```\n\nSelect from recommended patterns:\n- **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB\n- **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions\n- **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache\n- **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito\n\nSee `references\u002Farchitecture_patterns.md` for detailed pattern specifications.\n\n### Step 3: Generate IaC Templates\n\nCreate infrastructure-as-code for the selected pattern:\n\n```bash\n# Serverless stack (CloudFormation)\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n\n# Output: CloudFormation YAML template ready to deploy\n```\n\n### Step 4: Review Costs\n\nAnalyze estimated costs and optimization opportunities:\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources current_setup.json --monthly-spend 2000\n```\n\nOutput includes:\n- Monthly cost breakdown by service\n- Right-sizing recommendations\n- Savings Plans opportunities\n- Potential monthly savings\n\n### Step 5: Deploy\n\nDeploy the generated infrastructure:\n\n```bash\n# CloudFormation\naws cloudformation create-stack \\\n  --stack-name my-app-stack \\\n  --template-body file:\u002F\u002Ftemplate.yaml \\\n  --capabilities CAPABILITY_IAM\n\n# CDK\ncdk deploy\n\n# Terraform\nterraform init && terraform apply\n```\n\n### Step 6: Validate\n\nVerify deployment and set up monitoring:\n\n```bash\n# Check stack status\naws cloudformation describe-stacks --stack-name my-app-stack\n\n# Set up CloudWatch alarms\naws cloudwatch put-metric-alarm --alarm-name high-errors ...\n```\n\n---\n\n## Tools\n\n### architecture_designer.py\n\nGenerates architecture patterns based on requirements.\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json --output design.json\n```\n\n**Input:** JSON with app type, scale, budget, compliance needs\n**Output:** Recommended pattern, service stack, cost estimate, pros\u002Fcons\n\n### serverless_stack.py\n\nCreates serverless CloudFormation templates.\n\n```bash\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Output:** Production-ready CloudFormation YAML with:\n- API Gateway + Lambda\n- DynamoDB table\n- Cognito user pool\n- IAM roles with least privilege\n- CloudWatch logging\n\n### cost_optimizer.py\n\nAnalyzes costs and recommends optimizations.\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources inventory.json --monthly-spend 5000\n```\n\n**Output:** Recommendations for:\n- Idle resource removal\n- Instance right-sizing\n- Reserved capacity purchases\n- Storage tier transitions\n- NAT Gateway alternatives\n\n---\n\n## Quick Start\n\n### MVP Architecture (\u003C $100\u002Fmonth)\n\n```\nAsk: \"Design a serverless MVP backend for a mobile app with 1000 users\"\n\nResult:\n- Lambda + API Gateway for API\n- DynamoDB pay-per-request for data\n- Cognito for authentication\n- S3 + CloudFront for static assets\n- Estimated: $20-50\u002Fmonth\n```\n\n### Scaling Architecture ($500-2000\u002Fmonth)\n\n```\nAsk: \"Design a scalable architecture for a SaaS platform with 50k users\"\n\nResult:\n- ECS Fargate for containerized API\n- Aurora Serverless for relational data\n- ElastiCache for session caching\n- CloudFront for CDN\n- CodePipeline for CI\u002FCD\n- Multi-AZ deployment\n```\n\n### Cost Optimization\n\n```\nAsk: \"Optimize my AWS setup to reduce costs by 30%. Current spend: $3000\u002Fmonth\"\n\nProvide: Current resource inventory (EC2, RDS, S3, etc.)\n\nResult:\n- Idle resource identification\n- Right-sizing recommendations\n- Savings Plans analysis\n- Storage lifecycle policies\n- Target savings: $900\u002Fmonth\n```\n\n### IaC Generation\n\n```\nAsk: \"Generate CloudFormation for a three-tier web app with auto-scaling\"\n\nResult:\n- VPC with public\u002Fprivate subnets\n- ALB with HTTPS\n- ECS Fargate with auto-scaling\n- Aurora with read replicas\n- Security groups and IAM roles\n```\n\n---\n\n## Input Requirements\n\nProvide these details for architecture design:\n\n| Requirement | Description | Example |\n|-------------|-------------|---------|\n| Application type | What you're building | SaaS platform, mobile backend |\n| Expected scale | Users, requests\u002Fsec | 10k users, 100 RPS |\n| Budget | Monthly AWS limit | $500\u002Fmonth max |\n| Team context | Size, AWS experience | 3 devs, intermediate |\n| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |\n| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |\n\n**JSON Format:**\n\n```json\n{\n  \"application_type\": \"saas_platform\",\n  \"expected_users\": 10000,\n  \"requests_per_second\": 100,\n  \"budget_monthly_usd\": 500,\n  \"team_size\": 3,\n  \"aws_experience\": \"intermediate\",\n  \"compliance\": [\"SOC2\"],\n  \"availability_sla\": \"99.9%\"\n}\n```\n\n---\n\n## Output Formats\n\n### Architecture Design\n\n- Pattern recommendation with rationale\n- Service stack diagram (ASCII)\n- Configuration specifications\n- Monthly cost estimate\n- Scaling characteristics\n- Trade-offs and limitations\n\n### IaC Templates\n\n- **CloudFormation YAML**: Production-ready SAM\u002FCFN templates\n- **CDK TypeScript**: Type-safe infrastructure code\n- **Terraform HCL**: Multi-cloud compatible configs\n\n### Cost Analysis\n\n- Current spend breakdown\n- Optimization recommendations with savings\n- Priority action list (high\u002Fmedium\u002Flow)\n- Implementation checklist\n\n---\n\n## Reference Documentation\n\n| Document | Contents |\n|----------|----------|\n| `references\u002Farchitecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region |\n| `references\u002Fservice_selection.md` | Decision matrices for compute, database, storage, messaging |\n| `references\u002Fbest_practices.md` | Serverless design, cost optimization, security hardening, scalability |\n\n---\n\n## Limitations\n\n- Lambda: 15-minute execution, 10GB memory max\n- API Gateway: 29-second timeout, 10MB payload\n- DynamoDB: 400KB item size, eventually consistent by default\n- Regional availability varies by service\n- Some services have AWS-specific 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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",[151],{"categoryNo":152,"categoryName":153,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[155,36,156,157,158,159,43,160,161,162,163,37,164,165,166,167,168,133,34],"dilemm ai","arena","analyze","browser","openclaw agent","automation","browser automation","bot","openclaw","strategy","headless","compete","submit","matchmaking",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":46},"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",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",[179,180,181,182,183,184,185,129,186,167,34,187,36,188,189],"arxiv paper reviews","reviews","arxiv","paper","crawler","papers","comments","fetch","read","http","via",{"skillNo":191,"name":171,"nameZh":6,"description":172,"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":46},"01M6G8HQJ7N7WMY6YV7D7SSJ16","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Farxiv-paper-reviews\u002FSKILL.md",[194],{"categoryNo":176,"categoryName":177,"taxonomyType":20,"taxonomyTypeDesc":21},[182,181,180,179,189,184,185,129,183,167,34,186,187,36,188],{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":202,"publishedAt":46},"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",[],[203,204,205,206,207,208,209,210,211,212,213,214,215],"git","summary","git summary","commits","repository","win32","status","linux","contributors","true","quick","get","branches",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":220,"categories":221,"keywords":226,"publishedAt":46},"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",[222,223],{"categoryNo":152,"categoryName":153,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":224,"categoryName":225,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000037","律师与法务",[227,228,229,230,129,231,232,51,233,187,234,235,236,237,238,142,239,240,241],"agent constitution","constitution","contracts","tree","query","base","log","check","Rules","projects","compliance","actions","state","governance","github"]