[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69A42B2NGY1S8ZCDMYKF6KF":3,"skill-same:01M69A42B2NGY1S8ZCDMYKF6KF":87},{"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":23,"publishedAt":28,"linkOnly":29,"markdownContent":30,"markdownBody":31,"frontMatter":32,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":40,"ownerHtmlUrl":41,"ownerBlogUrl":42,"ownerDescription":43,"updatedAt":44,"install":45,"resources":58},"01M69A42B2NGY1S8ZCDMYKF6KF","preset",null,"Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).","microsoft\u002Ffrontier-agentic-cobuild-rvas","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69A23QTQYW13MVW8CA2FFT5",7,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",[5,9,24,25,26,27],"deploy","capacity","customize","setup","2026-11-10 10:12:30",false,"---\nname: preset\ndescription: \"Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.1\"\n---\n\n# Deploy Model to Optimal Region\n\nAutomates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.\n\n## What This Skill Does\n\n1. Verifies Azure authentication and project scope\n2. Checks capacity in current project's region\n3. If no capacity: analyzes all regions and shows available alternatives\n4. Filters projects by selected region\n5. Supports creating new projects if needed\n6. Deploys model with GlobalStandard SKU\n7. Monitors deployment progress\n\n## Prerequisites\n\n- Azure CLI installed and configured\n- Active Azure subscription with Cognitive Services read\u002Fcreate permissions\n- Microsoft Foundry project resource ID (`PROJECT_RESOURCE_ID` env var or provided interactively)\n  - Format: `\u002Fsubscriptions\u002F{sub-id}\u002FresourceGroups\u002F{rg}\u002Fproviders\u002FMicrosoft.CognitiveServices\u002Faccounts\u002F{account}\u002Fprojects\u002F{project}`\n  - Found in: Microsoft Foundry portal → Project → Overview → Resource ID\n\n## Quick Workflow\n\n### Fast Path (Current Region Has Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region capacity\n→ 4. Deploy immediately\n```\n\n### Alternative Region Path (No Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region (no capacity)\n→ 4. Query all regions → 5. Show alternatives → 6. Select region + project\n→ 7. Deploy\n```\n\n---\n\n## Deployment Phases\n\n| Phase | Action | Key Commands |\n|-------|--------|-------------|\n| 1. Verify Auth | Check Azure CLI login and subscription | `az account show`, `az login` |\n| 2. Get Project | Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists | `az cognitiveservices account show` |\n| 3. Get Model | List available models, user selects model + version | `az cognitiveservices account list-models` |\n| 4. Check Current Region | Query capacity using GlobalStandard SKU | `az rest --method GET ...\u002FmodelCapacities` |\n| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |\n| 6. Select Region + Project | User picks region; find or create project | `az cognitiveservices account list`, `az cognitiveservices account create` |\n| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | `az cognitiveservices account deployment create` |\n\nFor detailed step-by-step instructions, see [workflow reference](references\u002Fworkflow.md).\n\n---\n\n## Error Handling\n\n| Error | Symptom | Resolution |\n|-------|---------|------------|\n| Auth failure | `az account show` returns error | Run `az login` then `az account set --subscription \u003Cid>` |\n| No quota | All regions show 0 capacity | Defer to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting; check existing deployments; try alternative models |\n| Model not found | Empty capacity list | Verify model name with `az cognitiveservices account list-models`; check case sensitivity |\n| Name conflict | \"deployment already exists\" | Append suffix to deployment name (handled automatically by `generate_deployment_name` script) |\n| Region unavailable | Region doesn't support model | Select a different region from the available list |\n| Permission denied | \"Forbidden\" or \"Unauthorized\" | Verify Cognitive Services Contributor role: `az role assignment list --assignee \u003Cuser>` |\n\n---\n\n## Advanced Usage\n\n```bash\n# Custom capacity\naz cognitiveservices account deployment create ... --sku-capacity \u003Cvalue>\n\n# Check deployment status\naz cognitiveservices account deployment show --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname> --query \"{Status:properties.provisioningState}\"\n\n# Delete deployment\naz cognitiveservices account deployment delete --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname>\n```\n\n## Notes\n\n- **SKU:** GlobalStandard only — **API Version:** 2024-10-01 (GA stable)\n\n---\n\n## Related Skills\n\n- **microsoft-foundry** - Parent skill for Microsoft Foundry operations\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill\n- **azure-quick-review** - Review Azure resources for compliance\n- **cost-estimation** - Estimate costs through the separately installed `azure-cost` plugin\n- **azure-validate** - Validate Azure infrastructure before deployment\n","# Deploy Model to Optimal Region\n\nAutomates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.\n\n## What This Skill Does\n\n1. Verifies Azure authentication and project scope\n2. Checks capacity in current project's region\n3. If no capacity: analyzes all regions and shows available alternatives\n4. Filters projects by selected region\n5. Supports creating new projects if needed\n6. Deploys model with GlobalStandard SKU\n7. Monitors deployment progress\n\n## Prerequisites\n\n- Azure CLI installed and configured\n- Active Azure subscription with Cognitive Services read\u002Fcreate permissions\n- Microsoft Foundry project resource ID (`PROJECT_RESOURCE_ID` env var or provided interactively)\n  - Format: `\u002Fsubscriptions\u002F{sub-id}\u002FresourceGroups\u002F{rg}\u002Fproviders\u002FMicrosoft.CognitiveServices\u002Faccounts\u002F{account}\u002Fprojects\u002F{project}`\n  - Found in: Microsoft Foundry portal → Project → Overview → Resource ID\n\n## Quick Workflow\n\n### Fast Path (Current Region Has Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region capacity\n→ 4. Deploy immediately\n```\n\n### Alternative Region Path (No Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region (no capacity)\n→ 4. Query all regions → 5. Show alternatives → 6. Select region + project\n→ 7. Deploy\n```\n\n---\n\n## Deployment Phases\n\n| Phase | Action | Key Commands |\n|-------|--------|-------------|\n| 1. Verify Auth | Check Azure CLI login and subscription | `az account show`, `az login` |\n| 2. Get Project | Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists | `az cognitiveservices account show` |\n| 3. Get Model | List available models, user selects model + version | `az cognitiveservices account list-models` |\n| 4. Check Current Region | Query capacity using GlobalStandard SKU | `az rest --method GET ...\u002FmodelCapacities` |\n| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |\n| 6. Select Region + Project | User picks region; find or create project | `az cognitiveservices account list`, `az cognitiveservices account create` |\n| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | `az cognitiveservices account deployment create` |\n\nFor detailed step-by-step instructions, see [workflow reference](references\u002Fworkflow.md).\n\n---\n\n## Error Handling\n\n| Error | Symptom | Resolution |\n|-------|---------|------------|\n| Auth failure | `az account show` returns error | Run `az login` then `az account set --subscription \u003Cid>` |\n| No quota | All regions show 0 capacity | Defer to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting; check existing deployments; try alternative models |\n| Model not found | Empty capacity list | Verify model name with `az cognitiveservices account list-models`; check case sensitivity |\n| Name conflict | \"deployment already exists\" | Append suffix to deployment name (handled automatically by `generate_deployment_name` script) |\n| Region unavailable | Region doesn't support model | Select a different region from the available list |\n| Permission denied | \"Forbidden\" or \"Unauthorized\" | Verify Cognitive Services Contributor role: `az role assignment list --assignee \u003Cuser>` |\n\n---\n\n## Advanced Usage\n\n```bash\n# Custom capacity\naz cognitiveservices account deployment create ... --sku-capacity \u003Cvalue>\n\n# Check deployment status\naz cognitiveservices account deployment show --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname> --query \"{Status:properties.provisioningState}\"\n\n# Delete deployment\naz cognitiveservices account deployment delete --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname>\n```\n\n## Notes\n\n- **SKU:** GlobalStandard only — **API Version:** 2024-10-01 (GA stable)\n\n---\n\n## Related Skills\n\n- **microsoft-foundry** - Parent skill for Microsoft Foundry operations\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill\n- **azure-quick-review** - Review Azure resources for compliance\n- **cost-estimation** - Estimate costs through the separately installed `azure-cost` plugin\n- **azure-validate** - Validate Azure infrastructure before deployment\n",{"name":5,"license":15,"metadata":33,"description":7},{"author":12,"version":34},"1.0.1","main","8d6f0b571ad0caa2de760fb8d17ce30802ac6fd5",".agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset",4,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas","https:\u002F\u002Fmicrosoft.github.io\u002Ffrontier-agentic-cobuild-rvas","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-13 22:01:42",{"source":8,"skillName":5,"commands":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add microsoft\u002Ffrontier-agentic-cobuild-rvas --skill preset",{"tool":51,"command":52},"bunx","bunx skills add microsoft\u002Ffrontier-agentic-cobuild-rvas --skill preset",{"tool":54,"command":55},"pnpm","pnpm dlx skills add microsoft\u002Ffrontier-agentic-cobuild-rvas --skill preset","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",true,[59,65,69,73,79,83],{"path":60,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":63,"sourceHtmlUrl":64},"EXAMPLES.md","REFERENCE","参考文档",2989,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FEXAMPLES.md",{"path":66,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":67,"sourceHtmlUrl":68},"references\u002Fpreset-workflow.md",22492,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002Freferences\u002Fpreset-workflow.md",{"path":70,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fworkflow.md",5763,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002Freferences\u002Fworkflow.md",{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"scripts\u002Fextract_env_vars.py","SCRIPT","脚本",102141,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-recipes\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fextract-python-environment-variables\u002Fscripts\u002Fextract_env_vars.py",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"tests\u002Fconftest.py",919,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-recipes\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fextract-python-environment-variables\u002Ftests\u002Fconftest.py",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"tests\u002Ftest_extract_env_vars.py",120324,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-recipes\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fextract-python-environment-variables\u002Ftests\u002Ftest_extract_env_vars.py",[88,105,127,139,164,178],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":97,"publishedAt":28},"01M69A663RB1G5CNRTT745BVDJ","idea-forge","Research a customer's business, industry, and publicly stated priorities on the web when the team needs AI opportunity ideas. Propose internal and external ideas by complexity and prepare a selected idea's unapproved brief for AI Agent Creator.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002Fplugins\u002Fagentic-cobuild\u002Fskills\u002Fidea-forge\u002FSKILL.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[98,99,100,101,102,103,104],"idea forge","prepare","customer","ai agent","web","research","ai agent creator",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":117,"publishedAt":28},"01M69A52G1N7MNJPB61KPQ69AZ","cloud-architecture-diagram","Create, edit, or review Azure and Microsoft ecosystem architecture, GitHub CI\u002FCD, and auth\u002FAPI diagrams as editable draw.io files; resolve Microsoft and GitHub icons. Use for architecture visualization, diagram critique, or missing icons. Azure DevOps only when explicitly requested. Not for competing-cloud architecture, deployment, live discovery, security certification, or unrelated charts.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002Fplugins\u002Fagentic-cobuild\u002Fskills\u002Fcloud-architecture-diagram\u002FSKILL.md",[111,114],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[118,119,120,121,122,123,124,125,126],"cloud architecture diagram","architecture","python","MCP","github","security","support","identity","troubleshoot",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":134,"publishedAt":28},"01M69A4Y2SFZC26YNCXWTNBF2T","ai-agent-creator","Architecture-first Microsoft-cloud AI design for new applications, platform-managed agents, or AI capabilities in existing systems. Use when discovery, a functional\u002Ftechnical specification, editable draw.io views, AI token and platform-consumption estimates, cost scenarios, and an implementation plan must precede code. Produces a mandatory cost-estimate.md for every confirmed architecture. Offers an optional wireframe prototype after package completion, then hands off; standalone SDK help, existing-agent operations, implementation, deployment, and troubleshooting use other workflows.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002Fplugins\u002Fagentic-cobuild\u002Fskills\u002Fai-agent-creator\u002FSKILL.md",[133],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[101,104,135,136,137,9,119,138],"design","workflows","sdk","handoff",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":143,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":157,"publishedAt":28},"01M69A4R5BRMJ1V985PPD8NWTZ","impeccable","Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fimpeccable\u002FSKILL.md",[146,149,152],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":153,"categoryName":154,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[141,158,159,160,135,161,162,119,163],"accessibility","init","frontend","components","performance","review",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":170,"publishedAt":28},"01M69A4P469G8RSJK087HY5TMP","foundry-workflows","Multi-agent orchestration in Microsoft Foundry — declarative workflow vs A2A tool call vs Connected Agents pattern; Microsoft Agent Framework (MAF) patterns including Magentic manager\u002Fplanner. Use when adapting a scenario to multi-agent orchestration.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Ffoundry-workflows\u002FSKILL.md",[],[136,171,172,173,174,175,176,177,9],"foundry","foundry workflows","microsoft foundry","agent framework","a2a","orchestration","workflow",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":28},"01M69A4M71DGSG4JVGG42X1A1J","foundry-toolboxes","Curate intent-based Microsoft Foundry Toolboxes (preview) — one MCP-compatible endpoint bundling MCP, Web Search, Azure AI Search, Code Interpreter, File Search, OpenAPI, A2A, Browser Automation, and Computer Use tools. Use when a scenario needs a toolbox integration.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Ffrontier-agentic-cobuild-rvas\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Ffoundry-toolboxes\u002FSKILL.md",[184],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[171,186,121,102,175,173,187,188,9,189],"foundry toolboxes","azure ai","automation","browser automation"]