[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AD7PFXR9KKV3JGKE3H6E2Y":3,"skill-same:01M6AD7PFXR9KKV3JGKE3H6E2Y":66},{"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":18,"publishedAt":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":41,"ownerHtmlUrl":42,"ownerBlogUrl":43,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6AD7PFXR9KKV3JGKE3H6E2Y","aws-well-architected-review",null,"Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41081,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Faws-well-architected-review\u002FSKILL.md",[],[19,20,21,22,23,9,24,25,26,27,28,29,30],"review","aws well architected review","well architected","aws","aws well architected","architecture","workload","github issues","issues","framework","iac","for","2026-11-10 10:12:30",false,"---\nname: aws-well-architected-review\ndescription: 'Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.'\n---\n\n# AWS Well-Architected Review\n\nThis workflow performs a structured AWS Well-Architected Framework (WAF) review against your workload's IaC files and deployed infrastructure. It identifies risks across all 6 WAF pillars and creates GitHub issues to track remediation.\n\n## Prerequisites\n- AWS CLI configured and authenticated\n- IaC files present in the repository (Terraform, CloudFormation, CDK, or SAM)\n- GitHub MCP server configured and authenticated\n\n## Workflow Steps\n\n### Step 1: Load Well-Architected Framework Reference\nFetch current AWS WAF best practices:\n- `https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fframework\u002Fwelcome.html`\n- Pillar-specific lenses relevant to the workload type (Serverless, SaaS, etc.)\n\n### Step 2: Discover IaC & Architecture\nScan the repository for IaC files:\n- Terraform: `**\u002F*.tf`\n- CloudFormation\u002FSAM: `**\u002F*.yaml`, `**\u002F*.json` (CFn templates)\n- CDK: `lib\u002F**\u002F*.ts`, `bin\u002F**\u002F*.ts`, `cdk.json`\n\nIdentify key AWS services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.\n\n### Step 3: Pillar-by-Pillar Review\n\n#### Pillar 1: Operational Excellence\n- [ ] All infrastructure defined as IaC (no manual console changes)\n- [ ] Consistent tagging strategy applied across all resources\n- [ ] CloudWatch alarms defined for key metrics\n- [ ] Automated deployment pipeline present (no manual deployments)\n- [ ] CloudTrail enabled for audit logging\n- [ ] Runbooks or operational documentation present\n\n#### Pillar 2: Security\n- [ ] IAM roles use least-privilege policies (no `*` actions without justification)\n- [ ] No hardcoded credentials in IaC or code\n- [ ] Secrets managed via Secrets Manager or SSM Parameter Store\n- [ ] S3 buckets have public access blocked and server-side encryption enabled\n- [ ] Sensitive resources placed in private subnets\n- [ ] Security groups restrict inbound to minimum required ports\u002FCIDRs\n- [ ] KMS encryption enabled for sensitive data stores (RDS, EBS, S3, SQS, DynamoDB)\n- [ ] SSL\u002FTLS enforced on all endpoints (`enforceSSL: true`)\n- [ ] GuardDuty enabled (`aws guardduty list-detectors`)\n- [ ] AWS WAF configured on public-facing APIs and CloudFront distributions\n- [ ] MFA delete enabled on critical S3 buckets\n\n#### Pillar 3: Reliability\n- [ ] Multi-AZ deployments for production databases (RDS Multi-AZ, DynamoDB Global Tables)\n- [ ] Auto Scaling configured with appropriate policies for EC2\u002FECS\n- [ ] S3 versioning and lifecycle policies configured\n- [ ] RDS automated backups enabled with appropriate retention period\n- [ ] DynamoDB Point-in-Time Recovery (PITR) enabled\n- [ ] Dead Letter Queues (DLQ) configured for Lambda, SQS, SNS\n- [ ] Route 53 health checks configured for DNS failover\n- [ ] Lambda reserved concurrency set to prevent noisy-neighbor throttling\n\n#### Pillar 4: Performance Efficiency\n- [ ] Right-sized instance types (Lambda memory, EC2 type, RDS class)\n- [ ] Graviton\u002FARM instances used where available (Lambda `arm64`, EC2 Graviton)\n- [ ] Caching implemented (ElastiCache, DAX, CloudFront, API Gateway caching)\n- [ ] CloudFront used for global static content delivery\n- [ ] Aurora Serverless or DynamoDB On-Demand for variable load patterns\n- [ ] Lambda Provisioned Concurrency for latency-critical synchronous paths\n\n#### Pillar 5: Cost Optimization\n- [ ] EC2 Reserved Instances or Savings Plans for steady-state workloads\n- [ ] S3 lifecycle policies moving data to cheaper storage tiers\n- [ ] Lambda `arm64` architecture adopted (20% cost reduction)\n- [ ] VPC Endpoints for S3\u002FDynamoDB to avoid NAT Gateway charges\n- [ ] gp2 EBS volumes migrated to gp3 (same performance, 20% cheaper)\n- [ ] Development\u002Ftest environments have auto-shutdown schedules\n- [ ] AWS Budgets and Cost Anomaly Detection configured\n- [ ] Unattached EBS volumes and idle EC2 instances identified\n\n#### Pillar 6: Sustainability\n- [ ] Graviton\u002FARM instances selected where available\n- [ ] Serverless\u002Fmanaged services preferred over always-on EC2\n- [ ] S3 lifecycle policies reduce unnecessary long-term data storage\n- [ ] Auto Scaling configured to avoid over-provisioning\n- [ ] Region selection considers AWS renewable energy commitments\n\n### Step 4: Risk Classification\nFor each finding, classify:\n- **High Risk**: Security vulnerability, single point of failure, no backup\u002Frecovery\n- **Medium Risk**: Suboptimal reliability, cost inefficiency, performance concern\n- **Low Risk**: Best practice deviation, minor optimization opportunity\n\n### Step 5: User Confirmation\n\n```\n?️ AWS Well-Architected Review Summary\n\n? Review Results:\n• IaC Files Analyzed: X\n• AWS Services Identified: Y\n• Total Findings: Z\n  • High Risk: A (immediate action required)\n  • Medium Risk: B (should address soon)\n  • Low Risk: C (nice to have)\n\n? Top High Risk Findings:\n1. [Pillar]: [Finding] — [Why it matters]\n2. [Pillar]: [Finding] — [Why it matters]\n\n? This will create Z individual GitHub issues + 1 EPIC issue.\n\n❓ Proceed with creating GitHub issues? (y\u002Fn)\n```\n\n### Step 6: Create Individual Finding Issues\nLabel with \"well-architected\" and the pillar name (e.g., \"security\", \"reliability\").\n\n**Title**: `[WAF-\u003CPILLAR>] [Brief Finding] — [Risk Level]`\n\n**Body**:\n````markdown\n## ?️ Well-Architected Finding: [Brief Title]\n\n**Pillar**: [Name] | **Risk Level**: [High\u002FMedium\u002FLow] | **Effort**: [Low\u002FMedium\u002FHigh]\n\n### ? Description\n[Clear explanation of the finding and why it matters]\n\n### ? Remediation\n\n**IaC Fix** (preferred):\n```hcl\n# Terraform example\nresource \"aws_s3_bucket_server_side_encryption_configuration\" \"example\" {\n  bucket = aws_s3_bucket.example.id\n  rule {\n    apply_server_side_encryption_by_default {\n      sse_algorithm = \"aws:kms\"\n    }\n  }\n}\n```\n\n**AWS CLI fallback**:\n```bash\naws s3api put-bucket-encryption --bucket \u003Cname> \\\n  --server-side-encryption-configuration '{\"Rules\":[{\"ApplyServerSideEncryptionByDefault\":{\"SSEAlgorithm\":\"aws:kms\"}}]}'\n```\n\n### ? AWS Reference\n- [WAF Best Practice Link]\n- [AWS Documentation Link]\n\n### ✅ Validation\n- [ ] Change implemented in IaC and deployed\n- [ ] AWS Config rule passes (if applicable)\n- [ ] Security Hub finding resolved (if applicable)\n\n**Well-Architected Question**: [WAF question this maps to]\n````\n\n### Step 7: Create EPIC Tracking Issue\nLabel with \"well-architected\" and \"epic\".\n\n**Title**: `[EPIC] AWS Well-Architected Review — X findings across 6 pillars`\n\n**Body**: Executive summary with pillar breakdown table (finding counts by pillar and risk level), Mermaid architecture diagram, prioritized checklist linking all individual issues (High → Medium → Low), and success criteria:\n- All High-risk findings resolved\n- Medium findings have accepted mitigation plans\n- No regression in existing CloudWatch alarms or Config rules\n\n## Error Handling\n- **No IaC Files Found**: Limit review to live resource discovery via AWS CLI and note the gap\n- **Insufficient AWS Permissions**: List required read-only permissions for the review\n- **GitHub Creation Failure**: Output all findings as formatted markdown to console\n\n## Success Criteria\n- ✅ All 6 WAF pillars reviewed against IaC and live infrastructure\n- ✅ All findings classified by risk level and pillar\n- ✅ Actionable remediation steps with IaC examples for each finding\n- ✅ GitHub issues created for team tracking\n- ✅ Architecture diagram generated for EPIC context\n- ✅ AWS documentation references included\n","# AWS Well-Architected Review\n\nThis workflow performs a structured AWS Well-Architected Framework (WAF) review against your workload's IaC files and deployed infrastructure. It identifies risks across all 6 WAF pillars and creates GitHub issues to track remediation.\n\n## Prerequisites\n- AWS CLI configured and authenticated\n- IaC files present in the repository (Terraform, CloudFormation, CDK, or SAM)\n- GitHub MCP server configured and authenticated\n\n## Workflow Steps\n\n### Step 1: Load Well-Architected Framework Reference\nFetch current AWS WAF best practices:\n- `https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fframework\u002Fwelcome.html`\n- Pillar-specific lenses relevant to the workload type (Serverless, SaaS, etc.)\n\n### Step 2: Discover IaC & Architecture\nScan the repository for IaC files:\n- Terraform: `**\u002F*.tf`\n- CloudFormation\u002FSAM: `**\u002F*.yaml`, `**\u002F*.json` (CFn templates)\n- CDK: `lib\u002F**\u002F*.ts`, `bin\u002F**\u002F*.ts`, `cdk.json`\n\nIdentify key AWS services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.\n\n### Step 3: Pillar-by-Pillar Review\n\n#### Pillar 1: Operational Excellence\n- [ ] All infrastructure defined as IaC (no manual console changes)\n- [ ] Consistent tagging strategy applied across all resources\n- [ ] CloudWatch alarms defined for key metrics\n- [ ] Automated deployment pipeline present (no manual deployments)\n- [ ] CloudTrail enabled for audit logging\n- [ ] Runbooks or operational documentation present\n\n#### Pillar 2: Security\n- [ ] IAM roles use least-privilege policies (no `*` actions without justification)\n- [ ] No hardcoded credentials in IaC or code\n- [ ] Secrets managed via Secrets Manager or SSM Parameter Store\n- [ ] S3 buckets have public access blocked and server-side encryption enabled\n- [ ] Sensitive resources placed in private subnets\n- [ ] Security groups restrict inbound to minimum required ports\u002FCIDRs\n- [ ] KMS encryption enabled for sensitive data stores (RDS, EBS, S3, SQS, DynamoDB)\n- [ ] SSL\u002FTLS enforced on all endpoints (`enforceSSL: true`)\n- [ ] GuardDuty enabled (`aws guardduty list-detectors`)\n- [ ] AWS WAF configured on public-facing APIs and CloudFront distributions\n- [ ] MFA delete enabled on critical S3 buckets\n\n#### Pillar 3: Reliability\n- [ ] Multi-AZ deployments for production databases (RDS Multi-AZ, DynamoDB Global Tables)\n- [ ] Auto Scaling configured with appropriate policies for EC2\u002FECS\n- [ ] S3 versioning and lifecycle policies configured\n- [ ] RDS automated backups enabled with appropriate retention period\n- [ ] DynamoDB Point-in-Time Recovery (PITR) enabled\n- [ ] Dead Letter Queues (DLQ) configured for Lambda, SQS, SNS\n- [ ] Route 53 health checks configured for DNS failover\n- [ ] Lambda reserved concurrency set to prevent noisy-neighbor throttling\n\n#### Pillar 4: Performance Efficiency\n- [ ] Right-sized instance types (Lambda memory, EC2 type, RDS class)\n- [ ] Graviton\u002FARM instances used where available (Lambda `arm64`, EC2 Graviton)\n- [ ] Caching implemented (ElastiCache, DAX, CloudFront, API Gateway caching)\n- [ ] CloudFront used for global static content delivery\n- [ ] Aurora Serverless or DynamoDB On-Demand for variable load patterns\n- [ ] Lambda Provisioned Concurrency for latency-critical synchronous paths\n\n#### Pillar 5: Cost Optimization\n- [ ] EC2 Reserved Instances or Savings Plans for steady-state workloads\n- [ ] S3 lifecycle policies moving data to cheaper storage tiers\n- [ ] Lambda `arm64` architecture adopted (20% cost reduction)\n- [ ] VPC Endpoints for S3\u002FDynamoDB to avoid NAT Gateway charges\n- [ ] gp2 EBS volumes migrated to gp3 (same performance, 20% cheaper)\n- [ ] Development\u002Ftest environments have auto-shutdown schedules\n- [ ] AWS Budgets and Cost Anomaly Detection configured\n- [ ] Unattached EBS volumes and idle EC2 instances identified\n\n#### Pillar 6: Sustainability\n- [ ] Graviton\u002FARM instances selected where available\n- [ ] Serverless\u002Fmanaged services preferred over always-on EC2\n- [ ] S3 lifecycle policies reduce unnecessary long-term data storage\n- [ ] Auto Scaling configured to avoid over-provisioning\n- [ ] Region selection considers AWS renewable energy commitments\n\n### Step 4: Risk Classification\nFor each finding, classify:\n- **High Risk**: Security vulnerability, single point of failure, no backup\u002Frecovery\n- **Medium Risk**: Suboptimal reliability, cost inefficiency, performance concern\n- **Low Risk**: Best practice deviation, minor optimization opportunity\n\n### Step 5: User Confirmation\n\n```\n?️ AWS Well-Architected Review Summary\n\n? Review Results:\n• IaC Files Analyzed: X\n• AWS Services Identified: Y\n• Total Findings: Z\n  • High Risk: A (immediate action required)\n  • Medium Risk: B (should address soon)\n  • Low Risk: C (nice to have)\n\n? Top High Risk Findings:\n1. [Pillar]: [Finding] — [Why it matters]\n2. [Pillar]: [Finding] — [Why it matters]\n\n? This will create Z individual GitHub issues + 1 EPIC issue.\n\n❓ Proceed with creating GitHub issues? (y\u002Fn)\n```\n\n### Step 6: Create Individual Finding Issues\nLabel with \"well-architected\" and the pillar name (e.g., \"security\", \"reliability\").\n\n**Title**: `[WAF-\u003CPILLAR>] [Brief Finding] — [Risk Level]`\n\n**Body**:\n````markdown\n## ?️ Well-Architected Finding: [Brief Title]\n\n**Pillar**: [Name] | **Risk Level**: [High\u002FMedium\u002FLow] | **Effort**: [Low\u002FMedium\u002FHigh]\n\n### ? Description\n[Clear explanation of the finding and why it matters]\n\n### ? Remediation\n\n**IaC Fix** (preferred):\n```hcl\n# Terraform example\nresource \"aws_s3_bucket_server_side_encryption_configuration\" \"example\" {\n  bucket = aws_s3_bucket.example.id\n  rule {\n    apply_server_side_encryption_by_default {\n      sse_algorithm = \"aws:kms\"\n    }\n  }\n}\n```\n\n**AWS CLI fallback**:\n```bash\naws s3api put-bucket-encryption --bucket \u003Cname> \\\n  --server-side-encryption-configuration '{\"Rules\":[{\"ApplyServerSideEncryptionByDefault\":{\"SSEAlgorithm\":\"aws:kms\"}}]}'\n```\n\n### ? AWS Reference\n- [WAF Best Practice Link]\n- [AWS Documentation Link]\n\n### ✅ Validation\n- [ ] Change implemented in IaC and deployed\n- [ ] AWS Config rule passes (if applicable)\n- [ ] Security Hub finding resolved (if applicable)\n\n**Well-Architected Question**: [WAF question this maps to]\n````\n\n### Step 7: Create EPIC Tracking Issue\nLabel with \"well-architected\" and \"epic\".\n\n**Title**: `[EPIC] AWS Well-Architected Review — X findings across 6 pillars`\n\n**Body**: Executive summary with pillar breakdown table (finding counts by pillar and risk level), Mermaid architecture diagram, prioritized checklist linking all individual issues (High → Medium → Low), and success criteria:\n- All High-risk findings resolved\n- Medium findings have accepted mitigation plans\n- No regression in existing CloudWatch alarms or Config rules\n\n## Error Handling\n- **No IaC Files Found**: Limit review to live resource discovery via AWS CLI and note the gap\n- **Insufficient AWS Permissions**: List required read-only permissions for the review\n- **GitHub Creation Failure**: Output all findings as formatted markdown to console\n\n## Success Criteria\n- ✅ All 6 WAF pillars reviewed against IaC and live infrastructure\n- ✅ All findings classified by risk level and pillar\n- ✅ Actionable remediation steps with IaC examples for each finding\n- ✅ GitHub issues created for team tracking\n- ✅ Architecture diagram generated for EPIC context\n- ✅ AWS documentation references included\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Faws-well-architected-review",5344,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-13 02:05:36",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add github\u002Fawesome-copilot --skill aws-well-architected-review",{"tool":52,"command":53},"bunx","bunx skills add github\u002Fawesome-copilot --skill aws-well-architected-review",{"tool":55,"command":56},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill aws-well-architected-review","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Faws-well-architected-review\u002FSKILL.md",true,[60],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"agents\u002Fopenai.yaml","REFERENCE","参考文档",140,"https:\u002F\u002Fgithub.com\u002Ffeniix\u002Fdotfiles\u002Fblob\u002Fmain\u002Fagents\u002Fskills\u002Fwriting-fragments\u002Fagents\u002Fopenai.yaml",[67,101,128,158,192,209],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":81,"publishedAt":31},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[73,78],{"categoryNo":74,"categoryName":75,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":79,"categoryName":80,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000023","Skill与智能体开发",[82,83,84,85,86,87,19,88,89,90,91,92,93,94,95,96,97,98,99,100],"supabase","rls","supabase rls","supabase rls policies","project","when","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions","with",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":108,"publishedAt":31},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[107],{"categoryNo":79,"categoryName":80,"taxonomyType":76,"taxonomyTypeDesc":77},[109,110,111,112,113,114,30,115,116,117,118,119,120,121,122,123,124,125,126,127],"distribution","skill distribution audit","audit","SET","deterministic","installer","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":140,"publishedAt":31},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[135],{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[141,142,143,144,145,146,30,86,147,148,149,150,151,152,153,97,154,155,156,157],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":162,"sourceHtmlUrl":163,"categories":164,"keywords":169,"publishedAt":31},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[165,168],{"categoryNo":166,"categoryName":167,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},[170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,87,185,186,187,188,189,30,190,191],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":196,"licenseFileUrl":197,"sourceHtmlUrl":198,"categories":199,"keywords":205,"publishedAt":31},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[200,201,204],{"categoryNo":166,"categoryName":167,"taxonomyType":76,"taxonomyTypeDesc":77},{"categoryNo":202,"categoryName":203,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":79,"categoryName":80,"taxonomyType":76,"taxonomyTypeDesc":77},[206,112,179,207,208],"resemble detect","https","identity",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":196,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":220,"publishedAt":31},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[216,219],{"categoryNo":217,"categoryName":218,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":79,"categoryName":80,"taxonomyType":76,"taxonomyTypeDesc":77},[221,222,223,224,142,19,9,111,225,226,207,227,228,229,230,231,232,233,234,235,236,237,95,238,239,240,241],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]