[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KF37YN6JRX7VR2WVRH7T03":3,"skill-same:01M6KF37YN6JRX7VR2WVRH7T03":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},"01M6KF37YN6JRX7VR2WVRH7T03","run2_brand-color-strategy",null,"Strategic color application following Anthropic brand standards for professional technical materials","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\u002Fb2-self-feedback-claude-haiku-4-5\u002Fanthropic-poster-design\u002Frun2_brand-color-strategy\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","strategy","color","run2 brand color strategy","technical","standards","application","professional","strategic","for","materials","anthropic brand","2026-11-10 10:12:30",false,"---\nname: run2_brand-color-strategy\ndescription: Strategic color application following Anthropic brand standards for professional technical materials\n---\n\n# Strategic Brand Color Application\n\n## Anthropic Brand Philosophy\n\n### Core Principles\n1. **Minimalism**: Use colors purposefully, never decoratively\n2. **Hierarchy**: Primary colors for dominant elements, accents for emphasis\n3. **Accessibility**: Sufficient contrast ratios (WCAG AA minimum 4.5:1)\n4. **Consistency**: Same colors convey same meaning across materials\n\n## Official Anthropic Palette (Verified)\n\n```\nIdentity Light (Background):    #F8F7F5  RGB(248, 247, 245) - Warm off-white\nCorporate Dark (Primary):       #1E1B35  RGB(30, 27, 53)    - Deep navy\nPrimary Accent (Interactive):   #9D4EDD  RGB(157, 78, 221)  - Vibrant purple\nSecondary Accent (Supporting):  #7B68EE  RGB(123, 104, 238) - Medium purple\nTertiary Accent (Tertiary UI):  #B19CD9  RGB(177, 156, 217) - Light purple\nMuted Mid Gray (Borders\u002FLines): #9E9E9E  RGB(158, 158, 158) - Neutral gray\n```\n\n## Strategic Application for Technical Materials\n\n### Layer Assignment (Device Components)\n- **Structural\u002FPrimary Element** (Casing): Corporate Dark (#1E1B35)\n  - Communicates robustness and primary structure\n\n- **Interface\u002FUser-Facing** (Ports\u002FConnectors): Primary Accent (#9D4EDD)\n  - Draws attention to interactive points\n\n- **Thermal\u002FPerformance**: Secondary Accent (#7B68EE)\n  - Communicates engineering sophistication\n\n- **Computing Core** (PCB): Tertiary Accent (#B19CD9)\n  - Subtle but distinct from other components\n\n- **Power** (Battery): Corporate Dark (#1E1B35)\n  - Matches casing for structural stability perception\n\n### Color Contrast Verification\n\nFor text on colors:\n```\nDark Text on Identity Light:     Contrast = 12.5:1   ✓ Excellent\nWhite Text on Corporate Dark:    Contrast = 15:1     ✓ Excellent\nWhite Text on Primary Accent:    Contrast = 3.2:1    ✗ Needs alternative\nDark Text on Tertiary Accent:    Contrast = 7.8:1    ✓ Good\nGray on Identity Light:          Contrast = 2.8:1    ✓ Acceptable for supplementary\n```\n\n## Implementation Strategy\n\n### Title Area\n- Background: Identity Light (#F8F7F5)\n- Text: Corporate Dark (#1E1B35)\n- Optional: Subtle accent underline in Primary (#9D4EDD)\n\n### Component Labels\n- Text: Corporate Dark (#1E1B35)\n- Background: None (transparent)\n- Accent: Use Primary for emphasis callouts\n\n### Annotation Leader Lines\n- Color: Muted Mid Gray (#9E9E9E)\n- Width: 1-2px (minimalist approach)\n- Dots: Same gray with 4-6px diameter\n\n### Accent Highlights\n- On Primary Accent components: Use white (high contrast)\n- On Secondary Accent: Use white or Corporate Dark\n- On Tertiary Accent: Use Corporate Dark or medium gray\n\n## Color Function Pattern\n\n```python\ndef get_text_color_for_background(bg_hex):\n    \"\"\"Return appropriate text color based on background brightness\"\"\"\n    # Extract RGB\n    r, g, b = tuple(int(bg_hex[i:i+2], 16) for i in (0, 2, 4))\n    # Calculate luminance (WCAG formula)\n    luminance = (0.299*r + 0.587*g + 0.114*b) \u002F 255\n    # Return dark or light text\n    return DARK_TEXT_RGB if luminance > 0.5 else WHITE_RGB\n```\n\n## Minimalism Enforcement\n\n### Color Reduction\n- Maximum 5 colors per material (including neutral)\n- Never use more than 3 accent colors\n- Reserved palette prevents arbitrary color choices\n\n### Saturation Control\n- Primary Accent: Full saturation (emphasis)\n- Secondary\u002FTertiary: Slightly muted (supporting role)\n- Accent overlays: Rarely exceed 80% saturation\n\n### Visual Weight\n- Corporate Dark: Heavy visual weight (use sparingly)\n- Accents: Distribute evenly to avoid imbalance\n- Gray: Use for negative space and connections\n","# Strategic Brand Color Application\n\n## Anthropic Brand Philosophy\n\n### Core Principles\n1. **Minimalism**: Use colors purposefully, never decoratively\n2. **Hierarchy**: Primary colors for dominant elements, accents for emphasis\n3. **Accessibility**: Sufficient contrast ratios (WCAG AA minimum 4.5:1)\n4. **Consistency**: Same colors convey same meaning across materials\n\n## Official Anthropic Palette (Verified)\n\n```\nIdentity Light (Background):    #F8F7F5  RGB(248, 247, 245) - Warm off-white\nCorporate Dark (Primary):       #1E1B35  RGB(30, 27, 53)    - Deep navy\nPrimary Accent (Interactive):   #9D4EDD  RGB(157, 78, 221)  - Vibrant purple\nSecondary Accent (Supporting):  #7B68EE  RGB(123, 104, 238) - Medium purple\nTertiary Accent (Tertiary UI):  #B19CD9  RGB(177, 156, 217) - Light purple\nMuted Mid Gray (Borders\u002FLines): #9E9E9E  RGB(158, 158, 158) - Neutral gray\n```\n\n## Strategic Application for Technical Materials\n\n### Layer Assignment (Device Components)\n- **Structural\u002FPrimary Element** (Casing): Corporate Dark (#1E1B35)\n  - Communicates robustness and primary structure\n\n- **Interface\u002FUser-Facing** (Ports\u002FConnectors): Primary Accent (#9D4EDD)\n  - Draws attention to interactive points\n\n- **Thermal\u002FPerformance**: Secondary Accent (#7B68EE)\n  - Communicates engineering sophistication\n\n- **Computing Core** (PCB): Tertiary Accent (#B19CD9)\n  - Subtle but distinct from other components\n\n- **Power** (Battery): Corporate Dark (#1E1B35)\n  - Matches casing for structural stability perception\n\n### Color Contrast Verification\n\nFor text on colors:\n```\nDark Text on Identity Light:     Contrast = 12.5:1   ✓ Excellent\nWhite Text on Corporate Dark:    Contrast = 15:1     ✓ Excellent\nWhite Text on Primary Accent:    Contrast = 3.2:1    ✗ Needs alternative\nDark Text on Tertiary Accent:    Contrast = 7.8:1    ✓ Good\nGray on Identity Light:          Contrast = 2.8:1    ✓ Acceptable for supplementary\n```\n\n## Implementation Strategy\n\n### Title Area\n- Background: Identity Light (#F8F7F5)\n- Text: Corporate Dark (#1E1B35)\n- Optional: Subtle accent underline in Primary (#9D4EDD)\n\n### Component Labels\n- Text: Corporate Dark (#1E1B35)\n- Background: None (transparent)\n- Accent: Use Primary for emphasis callouts\n\n### Annotation Leader Lines\n- Color: Muted Mid Gray (#9E9E9E)\n- Width: 1-2px (minimalist approach)\n- Dots: Same gray with 4-6px diameter\n\n### Accent Highlights\n- On Primary Accent components: Use white (high contrast)\n- On Secondary Accent: Use white or Corporate Dark\n- On Tertiary Accent: Use Corporate Dark or medium gray\n\n## Color Function Pattern\n\n```python\ndef get_text_color_for_background(bg_hex):\n    \"\"\"Return appropriate text color based on background brightness\"\"\"\n    # Extract RGB\n    r, g, b = tuple(int(bg_hex[i:i+2], 16) for i in (0, 2, 4))\n    # Calculate luminance (WCAG formula)\n    luminance = (0.299*r + 0.587*g + 0.114*b) \u002F 255\n    # Return dark or light text\n    return DARK_TEXT_RGB if luminance > 0.5 else WHITE_RGB\n```\n\n## Minimalism Enforcement\n\n### Color Reduction\n- Maximum 5 colors per material (including neutral)\n- Never use more than 3 accent colors\n- Reserved palette prevents arbitrary color choices\n\n### Saturation Control\n- Primary Accent: Full saturation (emphasis)\n- Secondary\u002FTertiary: Slightly muted (supporting role)\n- Accent overlays: Rarely exceed 80% saturation\n\n### Visual Weight\n- Corporate Dark: Heavy visual weight (use sparingly)\n- Accents: Distribute evenly to avoid imbalance\n- Gray: Use for negative space and connections\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-haiku-4-5\u002Fanthropic-poster-design\u002Frun2_brand-color-strategy",8,"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 run2_brand-color-strategy",{"tool":59,"command":60},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_brand-color-strategy",{"tool":62,"command":63},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_brand-color-strategy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-haiku-4-5\u002Fanthropic-poster-design\u002Frun2_brand-color-strategy\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",[94,95,96,97,98,99,100,101,102,37,103,104,105,106,107],"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",[],[115,116,117,118,119,120,121,97,122,123,98,124,37,125,95],"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",[],[133,134,135,37,136,137,138,139,140,141,142,143,124,144,145],"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},[154,145,135,134,140,139,136,155,156,37,157],"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",[],[165,135,139,166,154,145,37,134,167,168,169,170,171,172,136],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]