[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69A3YG5WMDXMMF8SD5MCJFS":3,"skill-same:01M69A3YG5WMDXMMF8SD5MCJFS":91},{"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":22,"linkOnly":23,"markdownContent":24,"markdownBody":25,"frontMatter":26,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":34,"ownerHtmlUrl":35,"ownerBlogUrl":36,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M69A3YG5WMDXMMF8SD5MCJFS","capacity",null,"Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.","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\u002Fcapacity\u002FSKILL.md",[],[5,9,19,20,21],"deploy","customize","preset","2026-11-10 10:12:30",false,"---\nname: capacity\ndescription: \"Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n---\n\n# Capacity Discovery\n\nFinds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.\n\n## Quick Reference\n\n| Property | Description |\n|----------|-------------|\n| **Purpose** | Find where you can deploy a model with sufficient capacity |\n| **Scope** | All regions and projects the user has access to |\n| **Output** | Ranked table of regions\u002Fprojects with available capacity |\n| **Action** | Read-only analysis — does NOT deploy. Hands off to preset or customize |\n| **Authentication** | Azure CLI (`az login`) |\n\n## When to Use This Skill\n\n- ✅ User asks \"where can I deploy gpt-4o?\"\n- ✅ User specifies a capacity target: \"find a region with 10K TPM for gpt-4o\"\n- ✅ User wants to compare availability: \"which regions have gpt-4o available?\"\n- ✅ User got a quota error and needs to find an alternative location\n- ✅ User asks \"best region and project for deploying model X\"\n\n**After discovery → hand off to [preset](..\u002Fpreset\u002FSKILL.md) or [customize](..\u002Fcustomize\u002FSKILL.md) for actual deployment.**\n\n## Scripts\n\nPre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `scripts\u002Fdiscover_and_rank.ps1` | Full discovery: capacity + projects + ranking | Primary script for capacity discovery |\n| `scripts\u002Fdiscover_and_rank.sh` | Same as above (bash) | Primary script for capacity discovery |\n| `scripts\u002Fquery_capacity.ps1` | Raw capacity query (no project matching) | Quick capacity check or version listing |\n| `scripts\u002Fquery_capacity.sh` | Same as above (bash) | Quick capacity check or version listing |\n\n## Workflow\n\n### Phase 1: Validate Prerequisites\n\n```bash\naz account show --query \"{Subscription:name, SubscriptionId:id}\" --output table\n```\n\n### Phase 2: Identify Model and Version\n\nExtract model name from user prompt. If version is unknown, query available versions:\n\n```powershell\n.\\scripts\\query_capacity.ps1 -ModelName \u003Cmodel-name>\n```\n```bash\n.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel-name>\n```\n\nThis lists available versions. Use the latest version unless user specifies otherwise.\n\n### Phase 3: Run Discovery\n\nRun the full discovery script with model name, version, and minimum capacity target:\n\n```powershell\n.\\scripts\\discover_and_rank.ps1 -ModelName \u003Cmodel-name> -ModelVersion \u003Cversion> -MinCapacity \u003Ctarget>\n```\n```bash\n.\u002Fscripts\u002Fdiscover_and_rank.sh \u003Cmodel-name> \u003Cversion> \u003Cmin-capacity>\n```\n\n> ? The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.\n\n### Phase 3.5: Validate Subscription Quota\n\nAfter discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.\n\n```powershell\n# For each candidate region from discovery results:\n$usageData = az cognitiveservices usage list --location \u003Cregion> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json\n\n# Check quota for each SKU the model supports\n# Quota names follow pattern: OpenAI.\u003CSKU>.\u003Cmodel-name>\n$usageEntry = $usageData | Where-Object { $_.name.value -eq \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" }\n\nif ($usageEntry) {\n  $quotaAvailable = $usageEntry.limit - $usageEntry.currentValue\n} else {\n  $quotaAvailable = 0  # No quota allocated\n}\n```\n```bash\n# For each candidate region from discovery results:\nusage_json=$(az cognitiveservices usage list --location \u003Cregion> --subscription \"$SUBSCRIPTION_ID\" -o json 2>\u002Fdev\u002Fnull)\n\n# Extract quota for specific SKU+model\nquota_available=$(echo \"$usage_json\" | jq -r --arg name \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" \\\n  '.[] | select(.name.value == $name) | .limit - .currentValue')\n```\n\n**Annotate discovery results:**\n\nAdd a \"Quota Available\" column to the ranked output from Phase 3:\n\n| Region | Available Capacity | Meets Target | Projects | Quota Available |\n|--------|-------------------|--------------|----------|-----------------|\n| eastus2 | 120K TPM | ✅ | 3 | ✅ 80K |\n| westus3 | 90K TPM | ✅ | 1 | ❌ 0 (at limit) |\n| swedencentral | 100K TPM | ✅ | 0 | ✅ 100K |\n\nRegions\u002FSKUs where `quotaAvailable = 0` should be marked with ❌ in the results. If no region has available quota, hand off to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting.\n\n### Phase 4: Present Results and Hand Off\n\nAfter the script outputs the ranked table (now annotated with quota info), present it to the user and ask:\n\n1. ? **Quick deploy** to top recommendation with defaults → route to [preset](..\u002Fpreset\u002FSKILL.md)\n2. ⚙️ **Custom deploy** with version\u002FSKU\u002Fcapacity\u002FRAI selection → route to [customize](..\u002Fcustomize\u002FSKILL.md)\n3. ? **Check another model** or capacity target → re-run Phase 2\n4. ❌ Cancel\n\n### Phase 5: Confirm Project Before Deploying\n\nBefore handing off to preset or customize, **always confirm the target project** with the user. See the [Project Selection](..\u002FSKILL.md#project-selection-all-modes) rules in the parent router.\n\nIf the discovery table shows a sample project for the chosen region, suggest it as the default. Otherwise, query projects in that region and let the user pick.\n\n## Error Handling\n\n| Error | Cause | Resolution |\n|-------|-------|------------|\n| \"No capacity found\" | Model not available or all at quota | Hand off to [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting |\n| Script auth error | `az login` expired | Re-run `az login` |\n| Empty version list | Model not in region catalog | Try a different region: `.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel> \"\" eastus` |\n| \"No projects found\" | No AI Services resources | Guide to `project\u002Fcreate` skill or Azure Portal |\n\n## Related Skills\n\n- **[preset](..\u002Fpreset\u002FSKILL.md)** — Quick deployment after capacity discovery\n- **[customize](..\u002Fcustomize\u002FSKILL.md)** — Custom deployment after capacity discovery\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance\n","# Capacity Discovery\n\nFinds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.\n\n## Quick Reference\n\n| Property | Description |\n|----------|-------------|\n| **Purpose** | Find where you can deploy a model with sufficient capacity |\n| **Scope** | All regions and projects the user has access to |\n| **Output** | Ranked table of regions\u002Fprojects with available capacity |\n| **Action** | Read-only analysis — does NOT deploy. Hands off to preset or customize |\n| **Authentication** | Azure CLI (`az login`) |\n\n## When to Use This Skill\n\n- ✅ User asks \"where can I deploy gpt-4o?\"\n- ✅ User specifies a capacity target: \"find a region with 10K TPM for gpt-4o\"\n- ✅ User wants to compare availability: \"which regions have gpt-4o available?\"\n- ✅ User got a quota error and needs to find an alternative location\n- ✅ User asks \"best region and project for deploying model X\"\n\n**After discovery → hand off to [preset](..\u002Fpreset\u002FSKILL.md) or [customize](..\u002Fcustomize\u002FSKILL.md) for actual deployment.**\n\n## Scripts\n\nPre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `scripts\u002Fdiscover_and_rank.ps1` | Full discovery: capacity + projects + ranking | Primary script for capacity discovery |\n| `scripts\u002Fdiscover_and_rank.sh` | Same as above (bash) | Primary script for capacity discovery |\n| `scripts\u002Fquery_capacity.ps1` | Raw capacity query (no project matching) | Quick capacity check or version listing |\n| `scripts\u002Fquery_capacity.sh` | Same as above (bash) | Quick capacity check or version listing |\n\n## Workflow\n\n### Phase 1: Validate Prerequisites\n\n```bash\naz account show --query \"{Subscription:name, SubscriptionId:id}\" --output table\n```\n\n### Phase 2: Identify Model and Version\n\nExtract model name from user prompt. If version is unknown, query available versions:\n\n```powershell\n.\\scripts\\query_capacity.ps1 -ModelName \u003Cmodel-name>\n```\n```bash\n.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel-name>\n```\n\nThis lists available versions. Use the latest version unless user specifies otherwise.\n\n### Phase 3: Run Discovery\n\nRun the full discovery script with model name, version, and minimum capacity target:\n\n```powershell\n.\\scripts\\discover_and_rank.ps1 -ModelName \u003Cmodel-name> -ModelVersion \u003Cversion> -MinCapacity \u003Ctarget>\n```\n```bash\n.\u002Fscripts\u002Fdiscover_and_rank.sh \u003Cmodel-name> \u003Cversion> \u003Cmin-capacity>\n```\n\n> ? The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.\n\n### Phase 3.5: Validate Subscription Quota\n\nAfter discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.\n\n```powershell\n# For each candidate region from discovery results:\n$usageData = az cognitiveservices usage list --location \u003Cregion> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json\n\n# Check quota for each SKU the model supports\n# Quota names follow pattern: OpenAI.\u003CSKU>.\u003Cmodel-name>\n$usageEntry = $usageData | Where-Object { $_.name.value -eq \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" }\n\nif ($usageEntry) {\n  $quotaAvailable = $usageEntry.limit - $usageEntry.currentValue\n} else {\n  $quotaAvailable = 0  # No quota allocated\n}\n```\n```bash\n# For each candidate region from discovery results:\nusage_json=$(az cognitiveservices usage list --location \u003Cregion> --subscription \"$SUBSCRIPTION_ID\" -o json 2>\u002Fdev\u002Fnull)\n\n# Extract quota for specific SKU+model\nquota_available=$(echo \"$usage_json\" | jq -r --arg name \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" \\\n  '.[] | select(.name.value == $name) | .limit - .currentValue')\n```\n\n**Annotate discovery results:**\n\nAdd a \"Quota Available\" column to the ranked output from Phase 3:\n\n| Region | Available Capacity | Meets Target | Projects | Quota Available |\n|--------|-------------------|--------------|----------|-----------------|\n| eastus2 | 120K TPM | ✅ | 3 | ✅ 80K |\n| westus3 | 90K TPM | ✅ | 1 | ❌ 0 (at limit) |\n| swedencentral | 100K TPM | ✅ | 0 | ✅ 100K |\n\nRegions\u002FSKUs where `quotaAvailable = 0` should be marked with ❌ in the results. If no region has available quota, hand off to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting.\n\n### Phase 4: Present Results and Hand Off\n\nAfter the script outputs the ranked table (now annotated with quota info), present it to the user and ask:\n\n1. ? **Quick deploy** to top recommendation with defaults → route to [preset](..\u002Fpreset\u002FSKILL.md)\n2. ⚙️ **Custom deploy** with version\u002FSKU\u002Fcapacity\u002FRAI selection → route to [customize](..\u002Fcustomize\u002FSKILL.md)\n3. ? **Check another model** or capacity target → re-run Phase 2\n4. ❌ Cancel\n\n### Phase 5: Confirm Project Before Deploying\n\nBefore handing off to preset or customize, **always confirm the target project** with the user. See the [Project Selection](..\u002FSKILL.md#project-selection-all-modes) rules in the parent router.\n\nIf the discovery table shows a sample project for the chosen region, suggest it as the default. Otherwise, query projects in that region and let the user pick.\n\n## Error Handling\n\n| Error | Cause | Resolution |\n|-------|-------|------------|\n| \"No capacity found\" | Model not available or all at quota | Hand off to [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting |\n| Script auth error | `az login` expired | Re-run `az login` |\n| Empty version list | Model not in region catalog | Try a different region: `.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel> \"\" eastus` |\n| \"No projects found\" | No AI Services resources | Guide to `project\u002Fcreate` skill or Azure Portal |\n\n## Related Skills\n\n- **[preset](..\u002Fpreset\u002FSKILL.md)** — Quick deployment after capacity discovery\n- **[customize](..\u002Fcustomize\u002FSKILL.md)** — Custom deployment after capacity discovery\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer 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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",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[104,105,106,107,108,109,110],"idea forge","ai agent creator","prepare","web","customer","ai agent","research",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":123,"publishedAt":22},"01M69A52G1N7MNJPB61KPQ69AZ","cloud-architecture-diagram","Create, edit, or troubleshoot Azure-first architecture diagrams as editable draw.io files, with AWS and multicloud topology support and identity, API, and CI\u002FCD flow diagrams. Use for architecture visualization, network boundaries, interaction flows, or missing diagram icons. Not for deploying infrastructure, live cloud 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",[117,120],{"categoryNo":118,"categoryName":119,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":121,"categoryName":122,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发",[124,125,126,127,128,129,130,131,132],"architecture","cloud architecture diagram","github","MCP","python","security","support","troubleshoot","identity",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":22},"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. Ends with a reconciled architecture-package handoff; 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",[139],{"categoryNo":121,"categoryName":122,"taxonomyType":101,"taxonomyTypeDesc":102},[109,105,141,9,142,124,143,144],"sdk","workflows","handoff","design",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":163,"publishedAt":22},"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",[152,155,158],{"categoryNo":153,"categoryName":154,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":156,"categoryName":157,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":159,"categoryName":160,"taxonomyType":161,"taxonomyTypeDesc":162},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[147,124,164,144,165,166,167,168,169],"frontend","review","performance","accessibility","components","init",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":22},"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",[],[177,142,178,179,180,181,182,9,183],"foundry workflows","foundry","microsoft foundry","a2a","orchestration","workflow","agent framework",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":191,"publishedAt":22},"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",[190],{"categoryNo":121,"categoryName":122,"taxonomyType":101,"taxonomyTypeDesc":102},[178,192,193,180,194,107,9,179,195,127],"foundry toolboxes","browser automation","automation","azure ai"]