[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KH03VGQ4AG4ST6BAWT02S3":3,"skill-same:01M6KH03VGQ4AG4ST6BAWT02S3":67},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":50,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M6KH03VGQ4AG4ST6BAWT02S3","anthropic-brand-typography",null,"Use this skill when selecting and applying fonts for Anthropic brand materials. Specifies the official heading and body typefaces with correct fallback chains.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-opus-4-6\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-typography\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],"brand","typography","anthropic brand","anthropic brand typography","official","correct","use","fallback","with","fonts","for","materials","2026-11-10 10:12:30",false,"---\nname: anthropic-brand-typography\ndescription: Use this skill when selecting and applying fonts for Anthropic brand materials. Specifies the official heading and body typefaces with correct fallback chains.\n---\n\n# Anthropic Brand Typography\n\n## Heading Font\n\n- **Primary**: **Poppins** (geometric sans-serif)\n- **Fallback**: Arial\n- **Usage**: All headings, product titles (e.g., \"NOVA\"), section headers\n- **Weight**: Bold (700) for primary titles; SemiBold (600) for subheadings\n- **Style**: Typically uppercase or title-case for product names\n\n## Body Font\n\n- **Primary**: **Lora** (serif)\n- **Fallback**: Georgia\n- **Usage**: Body text, annotations, technical descriptions, captions\n- **Weight**: Regular (400) for body; Medium (500) for emphasized annotations\n\n## Font Discovery Logic (Python \u002F Pillow)\n\nWhen rendering with Pillow, use this search order:\n\n```python\nimport os\nfrom PIL import ImageFont\n\ndef find_font(font_names, size=48):\n    \"\"\"Search for fonts in common system paths, return first found.\"\"\"\n    search_paths = [\n        \"\u002Fusr\u002Fshare\u002Ffonts\",\n        \"\u002Fusr\u002Flocal\u002Fshare\u002Ffonts\",\n        \"\u002Froot\u002F.fonts\",\n        \"\u002Froot\u002F.local\u002Fshare\u002Ffonts\",\n        \"\u002FSystem\u002FLibrary\u002FFonts\",\n    ]\n    for font_name in font_names:\n        # Try direct load\n        for ext in [\".ttf\", \".otf\"]:\n            try:\n                return ImageFont.truetype(font_name + ext, size), font_name\n            except (OSError, IOError):\n                pass\n        # Walk search paths\n        for base in search_paths:\n            if not os.path.isdir(base):\n                continue\n            for root, dirs, files in os.walk(base):\n                for f in files:\n                    if font_name.lower().replace(\" \", \"\") in f.lower().replace(\" \", \"\"):\n                        if f.endswith((\".ttf\", \".otf\")):\n                            try:\n                                path = os.path.join(root, f)\n                                return ImageFont.truetype(path, size), font_name\n                            except (OSError, IOError):\n                                pass\n    # Ultimate fallback: Pillow default\n    return ImageFont.load_default(), \"default\"\n\n# For headings:\nheading_font, heading_font_name = find_font([\"Poppins-Bold\", \"Poppins\", \"Arial-Bold\", \"Arial\"], size=72)\n\n# For body\u002Fannotations:\nbody_font, body_font_name = find_font([\"Lora-Regular\", \"Lora\", \"Georgia\", \"Arial\"], size=24)\n```\n\n## Important Notes\n\n- The `applied_heading_font` field in `design_parameters.json` must reflect whichever font was **actually loaded** — \"Poppins\" if found, otherwise the fallback name (e.g., \"Arial\").\n- **Never** use Styrene A, Inter, or IBM Plex Sans as Anthropic brand fonts.\n- Install Poppins via `pip install fonts-poppins` or download from Google Fonts if not present on the system. Attempt `apt-get install -y fonts-poppins` or manually download to `\u002Froot\u002F.fonts\u002F`.","# Anthropic Brand Typography\n\n## Heading Font\n\n- **Primary**: **Poppins** (geometric sans-serif)\n- **Fallback**: Arial\n- **Usage**: All headings, product titles (e.g., \"NOVA\"), section headers\n- **Weight**: Bold (700) for primary titles; SemiBold (600) for subheadings\n- **Style**: Typically uppercase or title-case for product names\n\n## Body Font\n\n- **Primary**: **Lora** (serif)\n- **Fallback**: Georgia\n- **Usage**: Body text, annotations, technical descriptions, captions\n- **Weight**: Regular (400) for body; Medium (500) for emphasized annotations\n\n## Font Discovery Logic (Python \u002F Pillow)\n\nWhen rendering with Pillow, use this search order:\n\n```python\nimport os\nfrom PIL import ImageFont\n\ndef find_font(font_names, size=48):\n    \"\"\"Search for fonts in common system paths, return first found.\"\"\"\n    search_paths = [\n        \"\u002Fusr\u002Fshare\u002Ffonts\",\n        \"\u002Fusr\u002Flocal\u002Fshare\u002Ffonts\",\n        \"\u002Froot\u002F.fonts\",\n        \"\u002Froot\u002F.local\u002Fshare\u002Ffonts\",\n        \"\u002FSystem\u002FLibrary\u002FFonts\",\n    ]\n    for font_name in font_names:\n        # Try direct load\n        for ext in [\".ttf\", \".otf\"]:\n            try:\n                return ImageFont.truetype(font_name + ext, size), font_name\n            except (OSError, IOError):\n                pass\n        # Walk search paths\n        for base in search_paths:\n            if not os.path.isdir(base):\n                continue\n            for root, dirs, files in os.walk(base):\n                for f in files:\n                    if font_name.lower().replace(\" \", \"\") in f.lower().replace(\" \", \"\"):\n                        if f.endswith((\".ttf\", \".otf\")):\n                            try:\n                                path = os.path.join(root, f)\n                                return ImageFont.truetype(path, size), font_name\n                            except (OSError, IOError):\n                                pass\n    # Ultimate fallback: Pillow default\n    return ImageFont.load_default(), \"default\"\n\n# For headings:\nheading_font, heading_font_name = find_font([\"Poppins-Bold\", \"Poppins\", \"Arial-Bold\", \"Arial\"], size=72)\n\n# For body\u002Fannotations:\nbody_font, body_font_name = find_font([\"Lora-Regular\", \"Lora\", \"Georgia\", \"Arial\"], size=24)\n```\n\n## Important Notes\n\n- The `applied_heading_font` field in `design_parameters.json` must reflect whichever font was **actually loaded** — \"Poppins\" if found, otherwise the fallback name (e.g., \"Arial\").\n- **Never** use Styrene A, Inter, or IBM Plex Sans as Anthropic brand fonts.\n- Install Poppins via `pip install fonts-poppins` or download from Google Fonts if not present on the system. Attempt `apt-get install -y fonts-poppins` or manually download to `\u002Froot\u002F.fonts\u002F`.",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-opus-4-6\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-typography",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":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill anthropic-brand-typography",{"tool":59,"command":60},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill anthropic-brand-typography",{"tool":62,"command":63},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill anthropic-brand-typography","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-opus-4-6\u002Fanthropic-poster-design\u002Frun2_anthropic-brand-typography\u002FSKILL.md",true,[],[68,84,108,126,146,158],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":77,"publishedAt":40},"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",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[78,79,80,81,82,83],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":93,"publishedAt":40},"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",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[94,95,96,97,98,99,100,101,102,38,103,104,105,106,107],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":40},"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",[],[115,116,117,118,119,120,121,97,122,123,98,124,38,125,95],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":40},"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",[],[133,134,135,38,136,137,138,139,140,141,142,143,124,144,145],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":153,"publishedAt":40},"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",[152],{"categoryNo":91,"categoryName":92,"taxonomyType":20,"taxonomyTypeDesc":21},[154,145,135,134,140,139,136,155,156,38,157],"glm calibration","setup","companion","metric",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":40},"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",[],[165,135,139,166,154,145,38,134,167,168,169,34,170,171,136],"glm basics","inspect","relevant","files","good","how","Evaluation"]