Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/event4u-app/agent-confignpx agentmods add skills/event4u-app/agent-config/brandWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/event4u-app/agent-config/brand)<a href="https://agentmods.dev/skills/event4u-app/agent-config/brand"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/brand.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01367 |
| Opus 5 | $0.00021 | $0.00683 |
| Sonnet 5 | $0.00008 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
brand scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
brand
The grounded source for brand decisions — a second instance of the ADR-061 corpus-grounding layer (
corpus-grounding), afterdesign-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-identitycommit to a direction — those skills consult this corpus first. - When
typography-systemneeds the brand-aware (archetype → pairing-filter) upgrade — query thetypographydomain here.
Procedure: consult the brand corpus
-
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. -
Read
confidence+ everyevidence_gapline before trusting any row — surface them; the human signs off on what the corpus could NOT support. -
Propose grounded options (archetype + voice + colour + logo + messaging), each cited per corpus row, with alternatives — the human confirms.
-
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.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 119 lines · 42 tokens per session scan A e682cbd12df6
brand is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 1,367 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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