ai-brand-kit
Build comprehensive AI-native brand asset systems that maintain consistency across all AI-generated content. Train AI tools on brand guidelines, create reusable prompt libraries, and manage visual/voice assets at scale. Use when ", " mentioned.
适合你,如果需要在AI生成内容中保持品牌视觉和语调统一
/ 通过 npx 安装 校验哈希
npx oh-my-skill add omer-metin/skills-for-antigravity/ai-brand-kit/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- omer-metin/skills-for-antigravity/ai-brand-kit/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify omer-metin/skills-for-antigravity/ai-brand-kit安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用
商店整理自技能原文 · 版本 e8dcf4e · 表述以原文为准它做什么
装上后,Claude 会依据品牌参考文件(patterns.md、sharp_edges.md、validations.md)来创建、诊断或审核品牌内容,确保一致性。
什么时候触发
当用户提及品牌相关的创建、诊断或审核任务(例如生成品牌文案、检查品牌合规性)时触发。
装好后可以这样说
技能原文 SKILL.md
Ai Brand Kit
Identity
Principles
- {'principle': 'Brand is encoded in prompts, not just documents', 'why': 'AI tools need actionable instructions, not passive PDFs. Every brand\nguideline must translate to reusable prompts that AI can execute.\nDocuments describe; prompts direct.\n'}
- {'principle': 'Consistency requires negative prompts', 'why': 'Telling AI what NOT to generate is as critical as what to generate.\nBrand guardrails prevent style drift. "Never use gradients" is as\nimportant as "Always use bold typography."\n'}
- {'principle': 'Visual style needs reference anchors', 'why': 'AI visual models learn from examples, not descriptions. Create a\ncurated set of 10-20 "brand anchor" images that capture your aesthetic.\nThese become your Midjourney style references and DALL-E training set.\n'}
- {'principle': 'Voice training requires volume', 'why': 'Brand voice emerges from patterns across 50+ examples, not 5. Feed\nAI your best performing copy, tweets, emails. More signal = better\nvoice capture. Quality matters but quantity enables learning.\n'}
- {'principle': 'Governance beats creativity without it', 'why': 'AI generates infinite variations. Without approval workflows and\nversion control, brand chaos ensues. Better to constrain early than\nclean up inconsistency later.\n'}
- {'principle': 'Brand evolves - AI should too', 'why': "Brands aren't static. Your AI training, prompts, and style references\nmust version and evolve. Treat brand assets like code: version control,\nchangelog, deprecation strategy.\n"}
- {'principle': 'Context > generic brand voice', 'why': '"Brand voice" is too broad. You need voice for social, email, docs,\nsupport, landing pages. Context-specific prompts beat one-size-fits-all.\nLinkedIn voice != Twitter voice.\n'}
- {'principle': 'Benchmark quality to prevent drift', 'why': 'Without measurable quality standards, AI output degrades over time.\nDefine 5-10 "gold standard" examples for each content type. New AI\noutput must match or exceed these benchmarks.\n'}
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
按 Apache-2.0 许可原样转载,未经改动 · 在 GitHub 查看 →
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