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Brand

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

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

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  • 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

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Grounded brand decisions from a curated corpus — archetype, voice, naming, colour psychology, logo-style fit, messaging frameworks, archetype→type mapping. Use to ground brand strategy and identity.

SKILL.md

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

brand

The grounded source for brand decisions — a second instance of the ADR-061 corpus-grounding layer (corpus-grounding), after design-intelligence. Branding is the layer that constrains UI: the corpus grounds brand strategy and identity decisions (archetype, voice, naming, colour, logo style, messaging, archetype→type filter) as a constraint set the human confirms — never the final brand. No forked engine; this plugs into the shared one via a manifest.

Corpus: 7 tabular CSVs under data/ — 12 brand archetypes, a voice-and-tone matrix, naming patterns, color psychology by industry, logo-style ↔ industry fit, messaging frameworks, and typography-principles (archetype → pairing-filter Grounding, the layer that upgrades typography-system stage-2). Provenance: ATTRIBUTION.md; manifest: data/manifest.json.

When to use

  • A brand decision needs grounding: which archetype fits, what voice/tone, how to name, which colour direction, which logo style, which messaging framework, or which type pairing-filter an archetype implies.
  • Before brand-strategy / brand-identity commit to a direction — those skills consult this corpus first.
  • When typography-system needs the brand-aware (archetype → pairing-filter) upgrade — query the typography domain here.

Procedure: consult the brand corpus

  1. Ground or search (paths resolve skill-relative; works from any cwd):

    ./scripts-run <skills-root>/corpus-grounding/scripts/ground search \
  --manifest <skills-root>/brand/data/manifest.json \
  "<brand brief: sector + intent + audience>" \
  [--domain archetype|voice|naming|color|logo|messaging|typography] \
  [--filter "Archetype Fit=Ruler"] [--json]

    <skills-root> is ~/.claude/skills/ for Claude Code installs, src/skills/ inside this repo.

  2. Read confidence + every evidence_gap line before trusting any row — surface them; the human signs off on what the corpus could NOT support.

  3. Propose grounded options (archetype + voice + colour + logo + messaging), each cited per corpus row, with alternatives — the human confirms.

  4. The confirmed selections become the brand token + voice constraint set that brand-to-tokens, brand-consistency, and pack-ai-image's brand-asset generation consume.

Output format

  1. Grounded brand candidates per domain (archetype, voice, naming, colour, logo style, messaging, type filter) — each selection cited per corpus row.
  2. The grounded output's confidence label + every evidence_gap line, verbatim.
  3. Alternatives per domain so the human can swap before confirming.

Do NOT

  • Do NOT let the corpus author final brand copy or names — it supplies constraint sets and patterns; final strings are agent-written and human-confirmed.
  • Do NOT fork the engine — plug in via the manifest (ADR-061 §2).
  • Do NOT override the consumer's existing brand tokens / voice profile with corpus rows — consumer brand is the source of truth; the corpus fills gaps (see brand-source-of-truth).
  • Do NOT hide low confidence — the user signs off on the gaps too.

Gotchas

  • Corpus grounds pre-action selection — not mid-task reference and not output validation (brand-consistency owns validation).
  • Keep queries brand-shaped ("luxury law firm rebrand", "playful kids snack brand") — generic words land on the default archetype domain.
  • An empty result is a legitimate outcome — surface the evidence gap and proceed on priors; never widen filters to force a hit.
  • BM25 drops tokens ≤2 chars — pad short queries ("B2B", "AI") with companions.

See also

Policies

  • Provenance: ATTRIBUTION.md — original-authored corpus from public brand frameworks; shared engine attributed in design-intelligence/ATTRIBUTION.md.
  • Refresh: quarterly per the manifest; re-review archetype↔type rows when the font-pairings Reference refreshes.

What ships with it: 10 files

42.3 KB alongside SKILL.md

data/

evals/

Gives 0 of the 12 instructions most marketing audience skills give in ~1.2k 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

  • search the brand corpus before committing to a direction
  • read confidence and every evidence_gap before trusting a row
  • surface low confidence and evidence gaps to the human
  • propose grounded brand options cited per corpus row
  • provide alternatives per domain for human confirmation
  • use confirmed selections as the brand token set

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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