Borrowing it
Nothing to install: this file belongs to MohamedAbdallah-14/prompt-to-asset. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MohamedAbdallah-14/prompt-to-asset/main/.claude/skills/brand-consistency/SKILL.mdgit clone --depth 1 https://github.com/MohamedAbdallah-14/prompt-to-assetWrote 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/mohamedabdallah-14/prompt-to-asset/brand-consistency)<a href="https://agentmods.dev/skills/mohamedabdallah-14/prompt-to-asset/brand-consistency"><img src="https://agentmods.dev/badge/skills/mohamedabdallah-14/prompt-to-asset/brand-consistency/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mohamedabdallah-14/prompt-to-asset/brand-consistency"><img src="https://agentmods.dev/badge/skills/mohamedabdallah-14/prompt-to-asset/brand-consistency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00096 | $0.02033 |
| Opus 5 | $0.00048 | $0.01017 |
| Sonnet 5 | $0.00019 | $0.00407 |
| Haiku 4.5 | $0.00010 | $0.00203 |
Grade A, and why
brand-consistency 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 12d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand consistency
Engaged when a user is generating more than one asset for the same brand, or when an existing brand.json / brand.md is in play. Wraps asset_brand_bundle_parse, asset_generate_*, and validation.
BrandBundle shape
palette:
- { name: primary, hex: "#0A1F44", usage: mark }
- { name: accent, hex: "#FF6B6B", usage: highlight }
- { name: neutral, hex: "#FAFAFA", usage: background }
typography:
primary: { family: "Geist Sans", fallback: "geometric sans-serif, bold" }
secondary: { family: "Geist Mono", fallback: "monospace" }
style_refs: ["assets/brand/ref-1.png", "assets/brand/ref-2.png"]
lora: "assets/brand/brand.safetensors" # optional Flux/SDXL
sref_code: "--sref 1234567890" # optional Midjourney
style_id: "uuid-of-recraft-style" # optional Recraft
do_not: ["drop shadows", "gradients", "photorealism"]
logo_mark: "assets/brand/mark.svg" # canonical mark for composition
Call asset_brand_bundle_parse({ source }) to build this from a brand.md, brand.json, DTCG tokens.json, or AdCP spec.
Palette enforcement per model (strongest → weakest)
| Provider | Mechanism | ΔE2000 typical |
|---|---|---|
| Recraft V3/V4 | controls.colors: ["#hex", …] (hard enforcement) |
1–5 |
| Flux.2 | JSON color_palette: ["#hex", …] |
3–7 |
| Ideogram 3 | style_reference_images (palette swatch PNG) or style_codes |
5–10 |
| Midjourney | prose color words + --sref <id> --sw 250–400 |
5–12 |
| gpt-image-1 | hex codes in prose, reinforced 2–3× | 5–15 |
| SDXL / Flux.1 | hex in prose + IP-Adapter palette swatch | 5–15 |
| Imagen / Gemini | prose + post-process recolor | 10–20 (post-fix required) |
Fallback that always works: K-means remap in LAB space + ΔE2000 validation. asset_validate flags drift; post-process recolors nearest palette entries.
Style reference management
- Canonical anchor — one
.pngor.svgthat defines the brand look. Required for consistency across >3 assets. - Accepted-asset promotion — after an asset passes validation + user acceptance, add it to
style_refs[]so subsequent generations see richer brand context. - CSD similarity score — Contrastive Style Descriptor embedding comparison. Threshold: ≥0.72 pass, 0.60–0.72 review, <0.60 fail. Tier-2 validation.
- Model-specific handles:
- Midjourney:
--sref <image>(loose) vs--cref <image>(character lock) vs--mref <image>(object lock). Use--sw 250for tight style,--sw 100for loose brand. - Recraft:
style_idis a persistent UUID bound to a trained brand style. - Flux + IP-Adapter: pass reference image; set
ip_adapter_weight: 0.7–0.9for style, lower for just color. - SDXL + LoRA: trigger word in prompt +
lora_scale: 0.7–1.0.
- Midjourney:
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.
- 12d ago First seen · 138 lines · 96 tokens per session scan A 38170e487307
brand-consistency is a skill published in the GitHub repository MohamedAbdallah-14/prompt-to-asset (19 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 2,033 once invoked, about $0.0005 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 2026-08-30.
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