[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGBTEZGS0VR433HN0VZM83":3,"skill-same:01M6KGBTEZGS0VR433HN0VZM83":70},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6KGBTEZGS0VR433HN0VZM83","run2_anthropic-brand-guidelines",null,"Official Anthropic brand colors, typography, and styling instructions for technical posters and presentations.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-guidelines\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"brand guidelines","guidelines","brand","anthropic brand guidelines","anthropic brand","run2 anthropic brand","run2 anthropic brand guidelines","official","presentations","technical","instructions","typography","styling","for","anthropic brand colors","2026-11-10 10:12:30",false,"---\nname: run2_anthropic-brand-guidelines\ndescription: Official Anthropic brand colors, typography, and styling instructions for technical posters and presentations.\n---\n\n# Anthropic Brand Guidelines\n\nThis skill documents the exact, official Anthropic visual identity parameters used for technical artifacts, exploded-view diagrams, and brand assets.\n\n## Official Color Palette\n\n- **Identity Light (Background)**: `#faf9f5`\n- **Corporate Dark**: `#141413`\n- **Primary Accent (Orange)**: `#d97757`\n- **Secondary Accent (Blue)**: `#6a9bcc`\n- **Tertiary Accent (Green)**: `#788c5d`\n- **Muted Mid Gray**: `#b0aea5`\n\n## Typography\n\nAnthropic pairs a geometric sans-serif for headings with a classic serif for body text.\n\n- **Heading Font**: `Poppins`\n- **Body Font**: `Lora`\n- **Fallbacks**: `Arial`, `Georgia`\n\n## Design Directives for Technical Posters\n\n- **Background**: Always use Identity Light (`#faf9f5`).\n- **Heading Block**: Place headings (like product names) in the top-left using Corporate Dark (`#141413`) and the `Poppins` font.\n- **Hardware Layers**: Use Corporate Dark for outer casings. Use Accents for internal PCBs and thermal units.\n- **Highlights**: Use Primary Accent (`#d97757`) for connectors and interaction points.\n- **Annotations**: Use Muted Mid Gray (`#b0aea5`) for leader lines and secondary text.\n- **Aesthetic**: Maintain a clean, minimalist, low-saturation look. Avoid glowing effects, neon styling, or heavy AI gradients.\n","# Anthropic Brand Guidelines\n\nThis skill documents the exact, official Anthropic visual identity parameters used for technical artifacts, exploded-view diagrams, and brand assets.\n\n## Official Color Palette\n\n- **Identity Light (Background)**: `#faf9f5`\n- **Corporate Dark**: `#141413`\n- **Primary Accent (Orange)**: `#d97757`\n- **Secondary Accent (Blue)**: `#6a9bcc`\n- **Tertiary Accent (Green)**: `#788c5d`\n- **Muted Mid Gray**: `#b0aea5`\n\n## Typography\n\nAnthropic pairs a geometric sans-serif for headings with a classic serif for body text.\n\n- **Heading Font**: `Poppins`\n- **Body Font**: `Lora`\n- **Fallbacks**: `Arial`, `Georgia`\n\n## Design Directives for Technical Posters\n\n- **Background**: Always use Identity Light (`#faf9f5`).\n- **Heading Block**: Place headings (like product names) in the top-left using Corporate Dark (`#141413`) and the `Poppins` font.\n- **Hardware Layers**: Use Corporate Dark for outer casings. Use Accents for internal PCBs and thermal units.\n- **Highlights**: Use Primary Accent (`#d97757`) for connectors and interaction points.\n- **Annotations**: Use Muted Mid Gray (`#b0aea5`) for leader lines and secondary text.\n- **Aesthetic**: Maintain a clean, minimalist, low-saturation look. Avoid glowing effects, neon styling, or heavy AI gradients.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-guidelines",7,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_anthropic-brand-guidelines",{"tool":62,"command":63},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_anthropic-brand-guidelines",{"tool":65,"command":66},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_anthropic-brand-guidelines","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-guidelines\u002FSKILL.md",true,[],[71,87,111,129,149,161],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":80,"publishedAt":43},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[77],{"categoryNo":78,"categoryName":79,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[81,82,83,84,85,86],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":96,"publishedAt":43},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[97,98,99,100,101,102,103,104,105,41,106,107,108,109,110],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":43},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[118,119,120,121,122,123,124,100,125,126,101,127,41,128,98],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":43},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[136,137,138,41,139,140,141,142,143,144,145,146,127,147,148],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":156,"publishedAt":43},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[155],{"categoryNo":94,"categoryName":95,"taxonomyType":20,"taxonomyTypeDesc":21},[157,148,138,137,143,142,139,158,159,41,160],"glm calibration","setup","companion","metric",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":43},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[168,138,142,169,157,148,41,137,170,171,172,173,174,175,139],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]