[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DBJYWRMKS0HTTD8ZH9330X":3,"skill-same:01M6DBJYWRMKS0HTTD8ZH9330X":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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6DBJYWRMKS0HTTD8ZH9330X","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).","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\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],"deploy","deploy model","model","tool","creation","project","do","check model","deployments","up","version","use","MCP","projects","discovery","SET","deployment","model deployment","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:\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   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\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- 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:\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   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\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- 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":52,"description":7},{"author":53,"version":54},"Microsoft","1.0.0","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill deploy-model",{"tool":67,"command":68},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill deploy-model",{"tool":70,"command":71},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill deploy-model","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",true,[75,81,85],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Fgenerate_deployment_url.ps1","SCRIPT","脚本",2368,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.ps1",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"scripts\u002Fgenerate_deployment_url.sh",2684,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.sh",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"TEST_PROMPTS.md","REFERENCE","参考文档",3367,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FTEST_PROMPTS.md",[92,113,137,164,177,193],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":47},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[99,100,101,102,103,104,105,106,107,38,108,109,110,111,112],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":120,"publishedAt":47},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[119],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[121,122,123,124,125,126,30,127,128,129,130,131,132,133,38,134,135,136,33,39],"openmm md","how","decision","pick","linux","analyze","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":149,"publishedAt":47},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[143,146],{"categoryNo":144,"categoryName":145,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[150,151,152,153,154,155,156,157,158,159,122,160,161,162,163],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"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":47},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[166,171,172,32,173,174,175,176],"setup","report","installation","project setup","issues","state",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":184,"publishedAt":47},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[183],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[179,185,104,186,187,188,38,189,190,191,192],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":194,"name":171,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":198,"publishedAt":47},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[171,32,136,199,174,200],"files","interactive"]