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ai-brand-kit skill

/skills/ai-brand-kit

This skill helps you build and maintain an AI-native brand asset system with prompts, references, and governance to ensure consistent outputs.

npx playbooks add skill omer-metin/skills-for-antigravity --skill ai-brand-kit

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
3.1 KB
---
name: ai-brand-kit
description: 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. 
---

# 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.

Overview

This skill builds comprehensive AI-native brand asset systems that keep AI-generated content consistent with your brand. It trains AI tools on brand guidelines, creates reusable prompt libraries, and manages visual and voice assets at scale. The goal is predictable, governed AI output across channels.

How this skill works

The skill converts brand rules into executable prompt patterns and negative prompts, then packages those into reusable libraries for copy and image generation. It creates a curated set of visual anchors and feeds high-quality voice examples to train or prompt-tune models. Governance features include versioning, approval workflows, and benchmark validations to catch drift.

When to use it

  • Launching or scaling AI-generated marketing or product content
  • Onboarding AI tools to an existing brand or merging brands
  • When you need consistent voice and visuals across many channels and creators
  • Before automating content at scale to prevent brand drift
  • When introducing new visual styles or evolving brand guidelines

Best practices

  • Encode brand rules as prompts and negative prompts rather than only as documents
  • Curate 10–20 representative visual anchor images for model conditioning
  • Provide 50+ high-quality voice examples across contexts (social, email, docs)
  • Define approval workflows, changelogs, and version control for brand assets
  • Create context-specific prompt templates (social ≠ email ≠ docs) and benchmark examples
  • Set 5–10 gold-standard examples per content type and validate new output against them

Example use cases

  • Create a reusable prompt library for product descriptions, landing pages, and support responses
  • Train a visual model on brand anchors to generate on-brand hero images and social art
  • Set up an approval pipeline that flags off-brand outputs and enforces negative-prompt guardrails
  • Version and evolve brand asset packs as the company repositions or adopts new aesthetics
  • Benchmark and validate AI copy for different channels to maintain consistent voice

FAQ

How many examples do I need to capture brand voice?

Aim for 50+ high-quality, context-labeled examples across channels; quantity with quality helps models learn consistent patterns.

What are negative prompts and why use them?

Negative prompts explicitly tell models what not to produce (phrasing, colors, elements). They prevent style drift and are as important as positive instructions.