[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CPRQW1P297F5V06VHEWDGA":3,"skill-same:01M6CPRQW1P297F5V06VHEWDGA":100},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":35,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6CPRQW1P297F5V06VHEWDGA","openai-figma",null,"Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting. (Imported from openai upstream skills)","khalidsaidi\u002Fskillrunner","khalidsaidi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F4368721?v=4","01M6CPK1G2TKAWA8BSNSANBNWJ","Khalid Saidi","01M6CPK1M7RGKV85142PWBEJ54",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-figma\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"openai figma","openai","figma","ids","imported","upstream","main","task","Nodes","use","fetch","knowledge","design","server","code implementation","code","translate","mcp server","github","MCP","2026-11-10 10:12:30",false,"---\nname: openai-figma\ndescription: \"Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting. (Imported from openai upstream skills)\"\nversion: \"1.0.0\"\ntags: [\"openai\", \"upstream\", \"imported\"]\nkind: knowledge\nrisk: low\navailability: advanced\ndocs:\n  source: \"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Ffigma\"\n---\n\n# openai-figma\n\nImported upstream skill.\n\n- Provider source: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Ffigma\n- Original skill name: figma\n\n## Upstream Content\n\n# Figma MCP\n\nUse the Figma MCP server for Figma-driven implementation. For setup and debugging details (env vars, config, verification), see `references\u002Ffigma-mcp-config.md`.\n\n## Figma MCP Integration Rules\nThese rules define how to translate Figma inputs into code for this project and must be followed for every Figma-driven change.\n\n### Required flow (do not skip)\n1. Run get_design_context first to fetch the structured representation for the exact node(s).\n2. If the response is too large or truncated, run get_metadata to get the high-level node map and then re-fetch only the required node(s) with get_design_context.\n3. Run get_screenshot for a visual reference of the node variant being implemented.\n4. Only after you have both get_design_context and get_screenshot, download any assets needed and start implementation.\n5. Translate the output (usually React + Tailwind) into this project's conventions, styles and framework. Reuse the project's color tokens, components, and typography wherever possible.\n6. Validate against Figma for 1:1 look and behavior before marking complete.\n\n### Implementation rules\n- Treat the Figma MCP output (React + Tailwind) as a representation of design and behavior, not as final code style.\n- Replace Tailwind utility classes with the project's preferred utilities\u002Fdesign-system tokens when applicable.\n- Reuse existing components (e.g., buttons, inputs, typography, icon wrappers) instead of duplicating functionality.\n- Use the project's color system, typography scale, and spacing tokens consistently.\n- Respect existing routing, state management, and data-fetch patterns already adopted in the repo.\n- Strive for 1:1 visual parity with the Figma design. When conflicts arise, prefer design-system tokens and adjust spacing or sizes minimally to match visuals.\n- Validate the final UI against the Figma screenshot for both look and behavior.\n\n### Asset handling\n- The Figma MCP Server provides an assets endpoint which can serve image and SVG assets.\n- IMPORTANT: If the Figma MCP Server returns a localhost source for an image or an SVG, use that image or SVG source directly.\n- IMPORTANT: DO NOT import\u002Fadd new icon packages, all the assets should be in the Figma payload.\n- IMPORTANT: do NOT use or create placeholders if a localhost source is provided.\n\n### Link-based prompting\n- The server is link-based: copy the Figma frame\u002Flayer link and give that URL to the MCP client when asking for implementation help.\n- The client cannot browse the URL but extracts the node ID from the link; always ensure the link points to the exact node\u002Fvariant you want.\n\n## References\n- `references\u002Ffigma-mcp-config.md` — setup, verification, troubleshooting, and link-based usage reminders.\n- `references\u002Ffigma-tools-and-prompts.md` — tool catalog and prompt patterns for selecting frameworks\u002Fcomponents and fetching metadata.\n","# openai-figma\n\nImported upstream skill.\n\n- Provider source: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Ffigma\n- Original skill name: figma\n\n## Upstream Content\n\n# Figma MCP\n\nUse the Figma MCP server for Figma-driven implementation. For setup and debugging details (env vars, config, verification), see `references\u002Ffigma-mcp-config.md`.\n\n## Figma MCP Integration Rules\nThese rules define how to translate Figma inputs into code for this project and must be followed for every Figma-driven change.\n\n### Required flow (do not skip)\n1. Run get_design_context first to fetch the structured representation for the exact node(s).\n2. If the response is too large or truncated, run get_metadata to get the high-level node map and then re-fetch only the required node(s) with get_design_context.\n3. Run get_screenshot for a visual reference of the node variant being implemented.\n4. Only after you have both get_design_context and get_screenshot, download any assets needed and start implementation.\n5. Translate the output (usually React + Tailwind) into this project's conventions, styles and framework. Reuse the project's color tokens, components, and typography wherever possible.\n6. Validate against Figma for 1:1 look and behavior before marking complete.\n\n### Implementation rules\n- Treat the Figma MCP output (React + Tailwind) as a representation of design and behavior, not as final code style.\n- Replace Tailwind utility classes with the project's preferred utilities\u002Fdesign-system tokens when applicable.\n- Reuse existing components (e.g., buttons, inputs, typography, icon wrappers) instead of duplicating functionality.\n- Use the project's color system, typography scale, and spacing tokens consistently.\n- Respect existing routing, state management, and data-fetch patterns already adopted in the repo.\n- Strive for 1:1 visual parity with the Figma design. When conflicts arise, prefer design-system tokens and adjust spacing or sizes minimally to match visuals.\n- Validate the final UI against the Figma screenshot for both look and behavior.\n\n### Asset handling\n- The Figma MCP Server provides an assets endpoint which can serve image and SVG assets.\n- IMPORTANT: If the Figma MCP Server returns a localhost source for an image or an SVG, use that image or SVG source directly.\n- IMPORTANT: DO NOT import\u002Fadd new icon packages, all the assets should be in the Figma payload.\n- IMPORTANT: do NOT use or create placeholders if a localhost source is provided.\n\n### Link-based prompting\n- The server is link-based: copy the Figma frame\u002Flayer link and give that URL to the MCP client when asking for implementation help.\n- The client cannot browse the URL but extracts the node ID from the link; always ensure the link points to the exact node\u002Fvariant you want.\n\n## References\n- `references\u002Ffigma-mcp-config.md` — setup, verification, troubleshooting, and link-based usage reminders.\n- `references\u002Ffigma-tools-and-prompts.md` — tool catalog and prompt patterns for selecting frameworks\u002Fcomponents and fetching metadata.\n",{"docs":54,"kind":40,"name":5,"risk":56,"tags":57,"version":58,"description":7,"availability":59},{"source":55},"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Ffigma","low",[30,34,33],"1.0.0","advanced","c4da92076cba308457cd72bd1ab06c087e2cdf96","registry\u002Fskills\u002Fopenai-figma",0,"https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi","https:\u002F\u002Fkhalidsaidi.com","Software engineer. Maintainer of SourceMint (SRCMNT), an open-source fixed-supply ERC-20 and Foundry tooling on Base.","2026-11-07 02:40:24",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add khalidsaidi\u002Fskillrunner --skill openai-figma",{"tool":74,"command":75},"bunx","bunx skills add khalidsaidi\u002Fskillrunner --skill openai-figma",{"tool":77,"command":78},"pnpm","pnpm dlx skills add khalidsaidi\u002Fskillrunner --skill openai-figma","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-figma\u002FSKILL.md",true,[82,88,92,96],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fcore\u002Fexamples.md","REFERENCE","参考文档",1594,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Flinkable-content-architecture\u002Freferences\u002Fcore\u002Fexamples.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fcore\u002Fknowledge.md",1641,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Flinkable-content-architecture\u002Freferences\u002Fcore\u002Fknowledge.md",{"path":93,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fcore\u002Frules.md",1347,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Flinkable-content-architecture\u002Freferences\u002Fcore\u002Frules.md",{"path":97,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":98,"sourceHtmlUrl":99},"workflows\u002Faudit-linkable-content-architecture.md",1840,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Flinkable-content-architecture\u002Fworkflows\u002Faudit-linkable-content-architecture.md",[101,118,127,146,155,173],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":49},"01M6CPY1N6W58XKWMN4356E8GF","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-yeet\u002Fupstream\u002FSKILL.md",[],[103,108,109,38,110,111,112,113,114,115,116,117,47],"only","cli","user","gh","open","flow","push","pull","pull request","commit",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":49},"01M6CPXZ5RN2ZB5A4Y65RE5Y5J","openai-yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`). (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-yeet\u002FSKILL.md",[],[103,30,125,114,115,116,117,47,109,126,33,34,35,108,38,110,111,40,112,113],"openai yeet","https",{"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":136,"publishedAt":49},"01M6CPXWGRW59XN1V109G172CV","vercel-deploy","Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\".","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-vercel-deploy\u002Fupstream\u002FSKILL.md",[133],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",[137,138,139,114,38,140,141,142,143,110,144,145],"vercel","vercel deploy","deploy","actions","applications","me","preview deployment","deployment","create",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":153,"publishedAt":49},"01M6CPXSZSZRPQ5TY73K5QWN1B","openai-vercel-deploy","Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\". (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-vercel-deploy\u002FSKILL.md",[152],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},[139,30,138,137,154,140,33,34,142,35,143,38,110,40,141,126,114,145,144,47],"openai vercel deploy",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":49},"01M6CPXQ4SRTZZZ0VTD7WZP0RN","transcribe","Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio\u002Fvideo, extract text from recordings, or label speakers in interviews or meetings.","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-transcribe\u002Fupstream\u002FSKILL.md",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[157,110,165,166,167,168,169,170,171,38,172],"text","Audio","Video","speech","extract","files","recordings","meetings",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":180,"publishedAt":49},"01M6CPXMG2SCTAA04EB3PDRRXN","openai-transcribe","Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio\u002Fvideo, extract text from recordings, or label speakers in interviews or meetings. (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-transcribe\u002FSKILL.md",[179],{"categoryNo":162,"categoryName":163,"taxonomyType":21,"taxonomyTypeDesc":22},[30,181,157,47,38,33,34,35,170,171,172,169,110,165,40,166,167,126,168],"openai transcribe"]