Brand identity

Skill event4u-app/agent-config/dist/agent-src/skills/brand-identity

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-identity

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What its author says it does

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Define a brand identity constraint set from a confirmed strategy — colour story, type story, logo direction, imagery direction. Defines the tokens that token emission and asset generation consume.

SKILL.md

5.5 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

brand-identity

Grounding + Method skill. Turns a confirmed brand strategy into an identity constraint set: colour story, type story, logo direction, imagery direction. It DEFINES tokens and constraints — it does not render marks. Dependency direction: pack-brand (B) exports constraints; pack-ai-image (A) consumes them. brand-to-tokens emits the DTCG token file from these constraints. logo-generation and brand-asset-generation generate the actual marks from these constraints. Never invert that direction.

When to use

After brand-strategy is confirmed (archetype, voice, positioning settled).
When deriving the colour story, type story, logo direction, or imagery direction for a project.
Before running brand-to-tokens to emit the DTCG token file.
Before handing constraints to logo-generation or brand-asset-generation in pack-ai-image.

Procedure

Receive the confirmed strategy — archetype, voice, positioning, and target sector from brand-strategy. Refuse to proceed if strategy is still a draft.
Ground the colour story via the brand corpus:

./scripts-run <skills-root>/corpus-grounding/scripts/ground search \
  --manifest <skills-root>/brand/data/manifest.json \
  "<archetype + sector>" --domain color --json

Read confidence and evidence_gap from the response. Record both verbatim in the output. Derive colour roles (primary, secondary, neutral, accent) and direction (temperature, contrast ratio floor, emotional register).

Ground the type story via:

./scripts-run <skills-root>/corpus-grounding/scripts/ground search \
  --manifest <skills-root>/brand/data/manifest.json \
  "<archetype + sector>" --domain typography --json

Output is a pairing-filter + heading/body class labels, not concrete tokens. Hand this filter to typography-system for the actual type tokens.

Ground the logo direction via:

./scripts-run <skills-root>/corpus-grounding/scripts/ground search \
  --manifest <skills-root>/brand/data/manifest.json \
  "<archetype + sector>" --domain logo --json

Capture mark style, form language, and vector requirement. Note: any mark that the consumer may need in editable form MUST be specified as editable vector (SVG/AI), not raster.

Derive imagery direction from archetype and sector context: subject matter, mood, composition style, colour treatment, and what to avoid.
Assemble the identity constraint set — the structured seed for downstream skills. Record confidence and evidence_gap verbatim from all three corpus calls.
Human confirms the constraint set before any downstream step runs.
Export — hand off: brand-to-tokens receives the colour + type constraints and emits .tokens.json (DTCG); logo-generation and brand-asset-generation receive the logo direction and imagery direction. Direction of flow: B (pack-brand) -> A (pack-ai-image).

Output format

Colour story — roles (primary, secondary, neutral, accent) with direction (temperature, contrast floor, register), cited from corpus with confidence score.
Type story — heading class and body class derived from the archetype pairing-filter; note that concrete tokens come from typography-system, not from this skill.
Logo direction — mark style, form language, vector requirement (editable SVG/AI where needed), and any explicit exclusions.
Imagery direction — subject matter, mood, composition style, colour treatment, and anti-patterns to avoid.
Confidence + evidence_gap — verbatim from all corpus calls; flag any domain where evidence_gap is high before the human confirmation step.
Handoff note — which constraints go to brand-to-tokens (colour + type) and which go to the generation skills in pack-ai-image (logo direction + imagery direction).

Do NOT

Generate the actual marks here — that is logo-generation and brand-asset-generation.
Invert the dependency direction — generation lives in pack-ai-image (A), not in pack-brand (B).
Ship a raster as a final logo where the consumer needs an editable vector mark.
Override an existing set of brand tokens on a live project without explicit user confirmation.

Gotcha

Identity DEFINES constraints; generation CONSUMES them. Keep the B->A direction in every handoff note.
A type story is a pairing-filter plus heading/body class labels — the concrete type tokens (scale, weight, line-height) come from typography-system, not from this skill.
Vector-vs-raster is a real decision for any mark: confirm with the user before recording the logo direction, because raster is irreversible for downstream editing needs.

What ships with it: 1 file

1.5 KB alongside SKILL.md

evals/

triggers.json1.5 KB