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

1. Receive the confirmed strategy — archetype, voice, positioning, and target sector from brand-strategy. Refuse to proceed if strategy is still a draft.

2. Ground the colour story via the brand corpus:

node <skills-root>/corpus-grounding/scripts/ground.mjs 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).

3. Ground the type story via:

node <skills-root>/corpus-grounding/scripts/ground.mjs 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.

4. Ground the logo direction via:

node <skills-root>/corpus-grounding/scripts/ground.mjs 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.

5. Derive imagery direction from archetype and sector context: subject matter, mood, composition style, colour treatment, and what to avoid.

6. Assemble the identity constraint set — the structured seed for downstream skills. Record confidence and evidence_gap verbatim from all three corpus calls.

7. Human confirms the constraint set before any downstream step runs.

8. 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

1. Colour story — roles (primary, secondary, neutral, accent) with direction (temperature, contrast floor, register), cited from corpus with confidence score.
2. 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.
3. Logo direction — mark style, form language, vector requirement (editable SVG/AI where needed), and any explicit exclusions.
4. Imagery direction — subject matter, mood, composition style, colour treatment, and anti-patterns to avoid.
5. Confidence + evidence_gap — verbatim from all corpus calls; flag any domain where evidence_gap is high before the human confirmation step.
6. 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.

See also

• brand-strategy — supplies the confirmed strategy.
• brand-to-tokens — emits the DTCG token source of truth.
• typography-system — turns the type story into type tokens.
• logo-generation — generates marks from this identity (pack-ai-image).
• type-design — when the type story is a custom family: the wordmark's lettering or a brief built into an original font (pack-frontend-design).
• brand — the corpus grounded against.