[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CQ9ZXBZRTVGJ9KWFD9DPVN":3,"skill-same:01M6CQ9ZXBZRTVGJ9KWFD9DPVN":108},{"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":26,"publishedAt":53,"linkOnly":54,"markdownContent":55,"markdownBody":56,"frontMatter":57,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":14,"repositoryHtmlUrl":64,"repositoryHomepageUrl":65,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6CQ9ZXBZRTVGJ9KWFD9DPVN","deploy-model",null,"Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent\u002Fcreate), project creation (use project\u002Fcreate).","Trollz1004\u002FANTIGRAVITY","Trollz1004","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F132442315?v=4","01M6CQ4Z97ERZA2CXR388F6NZ1","Joshua Coleman","01M6CQ4ZCYE67QPNWBBV8H9DBE",0,"MIT","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"deploy","deploy model","model","tool","listing","quick","availability","creation","project","across","do","intent","check model","up","list","MCP","projects","discovery","analysis","SET","openai","create","deployment","azure openai","azure","policy","2026-11-10 10:12:30",false,"---\nname: deploy-model\ndescription: 'Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent\u002Fcreate), project creation (use project\u002Fcreate).'\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: '1.0.0'\n---\n\n# Deploy Model\n\n> **Scope — read this first.** This skill creates model deployments **out-of-band** via Azure CLI \u002F MCP \u002F portal. For azd-managed Foundry projects (those scaffolded from `azd-ai-starter-basic` or via `azd ai agent init`), declare deployments in `azure.yaml services.ai-project.deployments[]` instead — `azd ai agent init` writes the entry from the sample manifest and `azd provision` creates the deployment through Bicep. See [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](..\u002F..\u002Ffoundry-agent\u002Fcreate\u002Fcreate-hosted.md) for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.\n\nUnified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.\n\n## Quick Reference\n\n| Mode                   | When to Use                                      | Sub-Skill                                |\n| ---------------------- | ------------------------------------------------ | ---------------------------------------- |\n| **Preset**             | Quick deployment, no customization needed        | [preset\u002FSKILL.md](preset\u002FSKILL.md)       |\n| **Customize**          | Full control: version, SKU, capacity, RAI policy | [customize\u002FSKILL.md](customize\u002FSKILL.md) |\n| **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity\u002FSKILL.md](capacity\u002FSKILL.md)   |\n\n## Intent Detection\n\nAnalyze the user's prompt and route to the correct mode:\n\n```\nUser Prompt\n    │\n    ├─ Simple deployment (no modifiers)\n    │  \"deploy gpt-4o\", \"set up a model\"\n    │  └─> PRESET mode\n    │\n    ├─ Customization keywords present\n    │  \"custom settings\", \"choose version\", \"select SKU\",\n    │  \"set capacity to X\", \"configure content filter\",\n    │  \"PTU deployment\", \"with specific quota\"\n    │  └─> CUSTOMIZE mode\n    │\n    ├─ Capacity\u002Favailability query\n    │  \"find where I can deploy\", \"check capacity\",\n    │  \"which region has X capacity\", \"best region for 10K TPM\",\n    │  \"where is this model available\"\n    │  └─> CAPACITY DISCOVERY mode\n    │\n    └─ Ambiguous (has capacity target + deploy intent)\n       \"deploy gpt-4o with 10K capacity to best region\"\n       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE\n```\n\n### Routing Rules\n\n| Signal in Prompt                                      | Route To                 | Reason                                    |\n| ----------------------------------------------------- | ------------------------ | ----------------------------------------- |\n| Just model name, no options                           | **Preset**               | User wants quick deployment               |\n| \"custom\", \"configure\", \"choose\", \"select\"             | **Customize**            | User wants control                        |\n| \"find\", \"check\", \"where\", \"which region\", \"available\" | **Capacity**             | User wants discovery                      |\n| Specific capacity number + \"best region\"              | **Capacity → Preset**    | Discover then deploy quickly              |\n| Specific capacity number + \"custom\" keywords          | **Capacity → Customize** | Discover then deploy with options         |\n| \"PTU\", \"provisioned throughput\"                       | **Customize**            | PTU requires SKU selection                |\n| \"optimal region\", \"best region\" (no capacity target)  | **Preset**               | Region optimization is preset's specialty |\n\n### Multi-Mode Chaining\n\nSome prompts require two modes in sequence:\n\n**Pattern: Capacity → Deploy**\nWhen a user specifies a capacity requirement AND wants deployment:\n\n1. Run **Capacity Discovery** to find regions\u002Fprojects with sufficient quota\n2. Present findings to user\n3. Ask: \"Would you like to deploy with **quick defaults** or **customize settings**?\"\n4. Route to **Preset** or **Customize** based on answer\n\n> ? **Tip:** If unsure which mode the user wants, default to **Preset** (quick deployment). Users who want customization will typically use explicit keywords like \"custom\", \"configure\", or \"with specific settings\".\n\n## Project Selection (All Modes)\n\nBefore any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).\n\n### Resolution Order\n\n1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default\n2. **Check user prompt** — if user named a specific project or region, use that\n3. **If neither** — query the user's projects and suggest the current one\n\n### Confirmation Step (Required)\n\n**Always confirm the target before deploying.** Show the user what will be used and give them a chance to change it:\n\n```\nDeploying to:\n  Project:  \u003Cproject-name>\n  Region:   \u003Cregion>\n  Resource: \u003Cresource-group>\n\nIs this correct? Or choose a different project:\n  1. ✅ Yes, deploy here (default)\n  2. ? Show me other projects in this region\n  3. ? Choose a different region\n```\n\nIf user picks option 2, show top 5 projects in that region:\n\n```\nProjects in \u003Cregion>:\n  1. project-alpha (rg-alpha)\n  2. project-beta (rg-beta)\n  3. project-gamma (rg-gamma)\n  ...\n```\n\n> ⚠️ **Never deploy without showing the user which project will be used.** This prevents accidental deployments to the wrong resource.\n\n## Pre-Deployment Validation (All Modes)\n\nBefore presenting any deployment options (SKU, capacity), always validate both of these:\n\n1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:\n\n   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n\n   Filter for the model, extract `.model.skus[].name` to get supported SKUs.\n\n2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:\n   ```bash\n   az cognitiveservices usage list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Match by usage name pattern `OpenAI.\u003CSKU>.\u003Cmodel-name>` (e.g., `OpenAI.GlobalStandard.gpt-4o`). Compute `available = limit - currentValue`.\n\n> ⚠️ **Warning:** Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.\n\n> ? **Quota management:** For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the [quota skill](..\u002F..\u002Fquota\u002Fquota.md) instead of duplicating that guidance inline.\n\n## Prerequisites\n\nAll deployment modes require:\n\n- Azure CLI installed and authenticated (`az login`)\n- Active Azure subscription with deployment permissions\n- Azure AI Foundry project resource ID (or agent will help discover it via `PROJECT_RESOURCE_ID` env var)\n\n## Sub-Skills\n\n- **[preset\u002FSKILL.md](preset\u002FSKILL.md)** — Quick deployment to optimal region with sensible defaults\n- **[customize\u002FSKILL.md](customize\u002FSKILL.md)** — Interactive guided flow with full configuration control\n- **[capacity\u002FSKILL.md](capacity\u002FSKILL.md)** — Discover available capacity across regions and projects\n","# Deploy Model\n\n> **Scope — read this first.** This skill creates model deployments **out-of-band** via Azure CLI \u002F MCP \u002F portal. For azd-managed Foundry projects (those scaffolded from `azd-ai-starter-basic` or via `azd ai agent init`), declare deployments in `azure.yaml services.ai-project.deployments[]` instead — `azd ai agent init` writes the entry from the sample manifest and `azd provision` creates the deployment through Bicep. See [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](..\u002F..\u002Ffoundry-agent\u002Fcreate\u002Fcreate-hosted.md) for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.\n\nUnified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.\n\n## Quick Reference\n\n| Mode                   | When to Use                                      | Sub-Skill                                |\n| ---------------------- | ------------------------------------------------ | ---------------------------------------- |\n| **Preset**             | Quick deployment, no customization needed        | [preset\u002FSKILL.md](preset\u002FSKILL.md)       |\n| **Customize**          | Full control: version, SKU, capacity, RAI policy | [customize\u002FSKILL.md](customize\u002FSKILL.md) |\n| **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity\u002FSKILL.md](capacity\u002FSKILL.md)   |\n\n## Intent Detection\n\nAnalyze the user's prompt and route to the correct mode:\n\n```\nUser Prompt\n    │\n    ├─ Simple deployment (no modifiers)\n    │  \"deploy gpt-4o\", \"set up a model\"\n    │  └─> PRESET mode\n    │\n    ├─ Customization keywords present\n    │  \"custom settings\", \"choose version\", \"select SKU\",\n    │  \"set capacity to X\", \"configure content filter\",\n    │  \"PTU deployment\", \"with specific quota\"\n    │  └─> CUSTOMIZE mode\n    │\n    ├─ Capacity\u002Favailability query\n    │  \"find where I can deploy\", \"check capacity\",\n    │  \"which region has X capacity\", \"best region for 10K TPM\",\n    │  \"where is this model available\"\n    │  └─> CAPACITY DISCOVERY mode\n    │\n    └─ Ambiguous (has capacity target + deploy intent)\n       \"deploy gpt-4o with 10K capacity to best region\"\n       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE\n```\n\n### Routing Rules\n\n| Signal in Prompt                                      | Route To                 | Reason                                    |\n| ----------------------------------------------------- | ------------------------ | ----------------------------------------- |\n| Just model name, no options                           | **Preset**               | User wants quick deployment               |\n| \"custom\", \"configure\", \"choose\", \"select\"             | **Customize**            | User wants control                        |\n| \"find\", \"check\", \"where\", \"which region\", \"available\" | **Capacity**             | User wants discovery                      |\n| Specific capacity number + \"best region\"              | **Capacity → Preset**    | Discover then deploy quickly              |\n| Specific capacity number + \"custom\" keywords          | **Capacity → Customize** | Discover then deploy with options         |\n| \"PTU\", \"provisioned throughput\"                       | **Customize**            | PTU requires SKU selection                |\n| \"optimal region\", \"best region\" (no capacity target)  | **Preset**               | Region optimization is preset's specialty |\n\n### Multi-Mode Chaining\n\nSome prompts require two modes in sequence:\n\n**Pattern: Capacity → Deploy**\nWhen a user specifies a capacity requirement AND wants deployment:\n\n1. Run **Capacity Discovery** to find regions\u002Fprojects with sufficient quota\n2. Present findings to user\n3. Ask: \"Would you like to deploy with **quick defaults** or **customize settings**?\"\n4. Route to **Preset** or **Customize** based on answer\n\n> ? **Tip:** If unsure which mode the user wants, default to **Preset** (quick deployment). Users who want customization will typically use explicit keywords like \"custom\", \"configure\", or \"with specific settings\".\n\n## Project Selection (All Modes)\n\nBefore any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).\n\n### Resolution Order\n\n1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default\n2. **Check user prompt** — if user named a specific project or region, use that\n3. **If neither** — query the user's projects and suggest the current one\n\n### Confirmation Step (Required)\n\n**Always confirm the target before deploying.** Show the user what will be used and give them a chance to change it:\n\n```\nDeploying to:\n  Project:  \u003Cproject-name>\n  Region:   \u003Cregion>\n  Resource: \u003Cresource-group>\n\nIs this correct? Or choose a different project:\n  1. ✅ Yes, deploy here (default)\n  2. ? Show me other projects in this region\n  3. ? Choose a different region\n```\n\nIf user picks option 2, show top 5 projects in that region:\n\n```\nProjects in \u003Cregion>:\n  1. project-alpha (rg-alpha)\n  2. project-beta (rg-beta)\n  3. project-gamma (rg-gamma)\n  ...\n```\n\n> ⚠️ **Never deploy without showing the user which project will be used.** This prevents accidental deployments to the wrong resource.\n\n## Pre-Deployment Validation (All Modes)\n\nBefore presenting any deployment options (SKU, capacity), always validate both of these:\n\n1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:\n\n   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n\n   Filter for the model, extract `.model.skus[].name` to get supported SKUs.\n\n2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:\n   ```bash\n   az cognitiveservices usage list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Match by usage name pattern `OpenAI.\u003CSKU>.\u003Cmodel-name>` (e.g., `OpenAI.GlobalStandard.gpt-4o`). Compute `available = limit - currentValue`.\n\n> ⚠️ **Warning:** Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.\n\n> ? **Quota management:** For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the [quota skill](..\u002F..\u002Fquota\u002Fquota.md) instead of duplicating that guidance inline.\n\n## Prerequisites\n\nAll deployment modes require:\n\n- Azure CLI installed and authenticated (`az login`)\n- Active Azure subscription with deployment permissions\n- Azure AI Foundry project resource ID (or agent will help discover it via `PROJECT_RESOURCE_ID` env var)\n\n## Sub-Skills\n\n- **[preset\u002FSKILL.md](preset\u002FSKILL.md)** — Quick deployment to optimal region with sensible defaults\n- **[customize\u002FSKILL.md](customize\u002FSKILL.md)** — Interactive guided flow with full configuration control\n- **[capacity\u002FSKILL.md](capacity\u002FSKILL.md)** — Discover available capacity across regions and projects\n",{"name":5,"license":15,"metadata":58,"description":7},{"author":59,"version":60},"Microsoft","1.0.0","main","6deb42cc8c67a99d7613cb5802f8bbe01a0f3d12",".agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY","https:\u002F\u002Fwww.ai-solutions.store","https:\u002F\u002Fgithub.com\u002FTrollz1004","https:\u002F\u002Fyouandinotai.com","Founder building YouAndINotAI, OnlineRecycle, and AIDoesItAll through Trash Or Treasure Online Recycler LLC.","2026-11-11 18:41:12",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add Trollz1004\u002FANTIGRAVITY --skill deploy-model",{"tool":76,"command":77},"bunx","bunx skills add Trollz1004\u002FANTIGRAVITY --skill deploy-model",{"tool":79,"command":80},"pnpm","pnpm dlx skills add Trollz1004\u002FANTIGRAVITY --skill deploy-model","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",true,[84,90,94,100,104],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",12542,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ffine-tuning-peft\u002Freferences\u002Fadvanced-usage.md",{"path":91,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Ftroubleshooting.md",10345,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ffine-tuning-peft\u002Freferences\u002Ftroubleshooting.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fgenerate_deployment_url.ps1","SCRIPT","脚本",2368,"https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.ps1",{"path":101,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Fgenerate_deployment_url.sh",2684,"https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.sh",{"path":105,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":106,"sourceHtmlUrl":107},"TEST_PROMPTS.md",5195,"https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FTEST_PROMPTS.md",[109,137,175,199,226,249],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":113,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":53},"01M6M5BT49KEBHJM9D0P5NM8ED","security-audit","Security guidance and vulnerability review for codebases, APIs, services, CLI tools, libraries, and daemons. Use for security questions, focused reviews, vulnerability research, security audits, or pen tests. Run the complete workflow only for explicit codebase audit or pen-test requests, full\u002Fcomprehensive\u002Fend-to-end reviews, or requested report artifacts.","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsecurity-audit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsecurity-audit\u002FSKILL.md",[],[117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"security audit","audit","security","reviews","end","only","workflow","vulnerability","review","libraries","cli","run","Tools","full","for","complete","security guidance","codebase audit","tests","use",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":141,"licenseFileUrl":142,"sourceHtmlUrl":143,"categories":144,"keywords":156,"publishedAt":53},"01M6M5B16Q534MKFHKBHRZ2773","impeccable","Use when the user wants to design, redesign, shape, critique, audit, polish, simplify, harden, animate, or otherwise improve a frontend interface: websites, landing pages, dashboards, product UI, components, forms, settings, onboarding, empty states, and native iOS or Android screens. Handles UX review, hierarchy, layout, typography, color, motion, UX copy, accessibility, responsive behavior, edge cases, and capturing or documenting design systems (DESIGN.md, tokens). Also for making bland UI bolder, loud UI quieter, or ambitious visual effects. Not for backend-only or non-UI tasks.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fimpeccable\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fimpeccable\u002FSKILL.md",[145,148,151],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[139,157,158,159,160,161,162,163,122,164,165,166,167,168,125,169,170,171,172,173,174],"ux copy","states","quieter","frontend","improve","design","interface","design systems","effects","simplify","tasks","components","hierarchy","critique","native","product","layout","when",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":184,"publishedAt":53},"01M6M5AHHV2NPZYVMW48HP7A6H","hallmark","Design skill for websites and new pages that look designed, not generated. Use when the user asks to build or design something new, or names Hallmark. Never use it on an existing design unless the user names Hallmark, as in `\u002Fhallmark redesign \u003Cpath>`.","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fhallmark\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fhallmark\u002FSKILL.md",[182,183],{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[177,185,186,131,187,188,162,189,136,190,191,192,118,193,194,195,196,197,198,174],"as","never","true","pages","generated","Existing","prompt","build","redesign","look","it","user","path","new",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":141,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":53},"01M6CQDXJ785JCD5PMKYD239ZZ","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[206,209],{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},[211,212,213,214,160,215,216,217,218,219,220,221,222,223,131,224,225],"webapp","testing","webapp testing","applications","debugging","toolkit","logs","browser","playwright","web","behavior","ui","screenshots","local","with",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":233,"publishedAt":53},"01M6CQ5HBDE9Q5WJZXQ7G72MYD","appinsights-instrumentation","Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. USE FOR: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices. DO NOT USE FOR: adding App Insights to my app (use azure-prepare), add telemetry to my project (use azure-prepare), add monitoring (use azure-prepare). This skill provides guidance—azure-prepare orchestrates component changes.","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fappinsights-instrumentation\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fappinsights-instrumentation\u002FSKILL.md",[],[234,235,37,51,236,136,237,238,239,240,241,242,243,244,35,245,246,247,248,131],"appinsights instrumentation","instrumentation","telemetry","monitoring","references","component","how","patterns","apm","sdk setup","app","insights","how to","my","add",{"skillNo":250,"name":251,"nameZh":6,"description":252,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":253,"categories":254,"keywords":268,"publishedAt":53},"01M6CQNQ5FNDP6K32RKXQ9X157","youtube-automation","YouTube automation: upload, SEO, thumbnails, scheduling, analytics.","https:\u002F\u002Fgithub.com\u002FTrollz1004\u002FANTIGRAVITY\u002Fblob\u002Fmain\u002Fops\u002Fskills\u002Fyoutube-automation\u002FSKILL.md",[255,258,261,262,265],{"categoryNo":256,"categoryName":257,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":259,"categoryName":260,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":263,"categoryName":264,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":266,"categoryName":267,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000035","市场营销",[269,270,271,272,273,274,275,276,277,278,279,280,281,282],"automation","youtube","youtube automation","windows","seo","linux","scheduling","thumbnails","hermes","analytics","hermes agent","content","macos","upload"]