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Brand asset generation

Skill event4u-app/agent-config/src/skills/brand-asset-generation

Universal AI Agent OS — audited skills, governance rules, replayable state. One contract, every host agent.

Install

npx -y skills add event4u-app/agent-config --skill brand-asset-generation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Generate brand assets — banners, social cards, CIP elements — with brand-token injection + provider routing. Use when generating a banner / social image / branded asset.

SKILL.md

4.7 KB, 972 tokens by cl100k_base, as published. Nobody here has run it

brand-asset-generation

Generate brand assets (banner, social card, CIP element) via structured prompting, brand-token injection, and provider routing. Rides on the existing pack-ai-image adapters — not a second image-gen stack.

When to use

User asks to generate a banner, social card, header image, or CIP (corporate identity) element.
Branded asset production where palette, typography, or voice must stay consistent.
When brand tokens are available and should drive the visual output.
When a brief alone (no tokens) still needs a governance-aware image output.

Procedure

Identify asset type and spec — determine format (banner, social card, CIP element), output dimensions (e.g. 1200×630 for Open Graph, 1080×1080 for square social), and target channel (web, print, social platform).
Inject brand tokens when present — if pack-brand is installed, load .tokens.json (colors, typography, voice). Feed hex values, font names, and tone keywords directly into the prompt. Without tokens, derive palette and type from the brief itself; raw generation works — output is brief-driven, not token-driven.
Route and prompt — delegate provider selection to image-provider-routing (text-in-image → Ideogram, photoreal product shot → Flux, etc.). Author the provider-specific prompt with the asset spec, injected tokens, and any negative constraints.
Dry-run and validate — invoke the adapter (scaffold-tier; see Gotcha). Confirm the returned dry-run plan matches the spec: dimensions, style intent, brand token usage.
Rights and AI-disclosure governance — run image-likeness-and-rights if the asset depicts a real person or brand mark. Attach the AI-generation disclosure footer per media-governance-routing before delivering output.

Output format

Asset spec — type, dimensions, channel, and routing rationale (which provider and why).
Prompt — final provider-specific prompt string with injected brand tokens (or brief-derived palette/type if no tokens). Include key params: aspect ratio, style keywords, negative prompts.
Adapter invocation / dry-run note — the dry-run plan returned by the adapter, or an explicit note: "adapter is experimental (scaffold-tier) — dry-run plan only; no rendered asset until promotion per provider-lifecycle-discipline."
Governance confirmation — rights check result and AI-disclosure footer.

Gotcha

Without a brand token layer the output is generic — feed the brief's exact palette (hex codes) and typography (font names or style descriptors) into the prompt. Vague color terms ("blue", "modern") produce inconsistent results. Brand tokens from pack-brand (Phase B of the brand pipeline) eliminate this gap; until that pack ships, rely on brief-supplied values.
Brand tokens come from pack-brand (Phase B) — this skill consumes tokens; it does not author them. If .tokens.json is absent, proceed brief-driven and note the gap.
Adapters are scaffold-tier (dry-run only) — all pack-ai-image adapters are experimental. This skill produces a blueprint and dry-run confirmation; actual renders require a maintainer to capture a smoke trace and promote the adapter to stable.

Do NOT

Do NOT invent brand colors or voice — use tokens from .tokens.json or explicit values from the brief. Guessing palette values produces off-brand output.
Do NOT omit the AI-generation disclosure — every delivered asset requires the disclosure footer per media-governance-routing, regardless of how generic the output appears.
Do NOT claim a rendered asset is produced while adapters are scaffold-tier — surface the dry-run caveat explicitly every time.

See also

logo-generation — logo-specific generation with vector and mark constraints.
image-generation — general-purpose image generation end-to-end.
image-provider-routing — select the right provider before writing the prompt.
image-likeness-and-rights — rights check before generating real-person likenesses or brand marks.

What ships with it: 1 file

863 B alongside SKILL.md

evals/

triggers.json863 B

Gives 0 of the 12 instructions most marketing audience skills give in 972 tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

Apply Poppins font to headingsin 41 of 690, across 6 files
Apply Lora font to body textin 41 of 690, across 6 files
Use Arial fallback for headingsin 39 of 690, across 4 files
Use Georgia fallback for body textin 39 of 690, across 4 files
Maintain text hierarchy and formattingin 39 of 690, across 4 files
Use accent colors for non-text shapesin 38 of 690, across 3 files
Use RGB values for precise color matchingin 38 of 690, across 3 files
Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
Use active voice instead of passive voicein 26 of 690, across 10 files
Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

identify asset type, dimensions, and channel
inject brand tokens from .tokens.json when available
derive palette and typography from the brief without tokens
route provider selection via image-provider-routing
author the provider-specific prompt with asset specs
invoke the adapter to confirm the dry-run plan

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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