Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add event4u-app/agent-config --skill brand-asset-generationgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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-asset-generation)<a href="https://agentmods.dev/skills/event4u-app/agent-config/brand-asset-generation"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/brand-asset-generation/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/event4u-app/agent-config/brand-asset-generation"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/brand-asset-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00037 | $0.01292 |
| Opus 5 | $0.00018 | $0.00646 |
| Sonnet 5 | $0.00007 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
brand-asset-generation 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
brand-asset-generation
Generate brand assets (banner, social card, CIP element) via structured prompting, brand-token injection, and provider routing. Rides on the existing pack-ai-image adapters — not a second image-gen stack.
When to use
- User asks to generate a banner, social card, header image, platform profile or cover image (a LinkedIn cover, an X header, a YouTube channel art), or a CIP (corporate identity) element.
- Branded asset production where palette, typography, or voice must stay consistent.
- When brand tokens are available and should drive the visual output.
- When a brief alone (no tokens) still needs a governance-aware image output.
Procedure
-
Identify asset type and spec — determine format (banner, social card, platform profile/cover image, CIP element), output dimensions (e.g. 1200×630 for Open Graph, 1080×1080 for square social), and target channel (web, print, social platform).
A platform cover is a dimension constraint, not a new asset class, and it is the one case where guessing the number is the whole failure: a cover rendered at the wrong aspect ratio is cropped by the platform, so the brand marks land outside the visible area and the asset is unusable rather than merely off-brand. Take the required dimensions from the platform's own current spec at generation time — never from memory, and never from a number written here, because these change without notice. If the spec cannot be established, say so and ask rather than emitting an asset that will be cropped.
-
Inject brand tokens when present — if
pack-brandis installed, load.tokens.json(colors, typography, voice). Feed hex values, font names, and tone keywords directly into the prompt. Without tokens, derive palette and type from the brief itself; raw generation works — output is brief-driven, not token-driven. -
Route and prompt — delegate provider selection to
image-provider-routing(text-in-image → Ideogram, photoreal product shot → Flux, etc.). Author the provider-specific prompt with the asset spec, injected tokens, and any negative constraints. -
Dry-run and validate — invoke the adapter (scaffold-tier; see Gotcha). Confirm the returned dry-run plan matches the spec: dimensions, style intent, brand token usage.
-
Rights and AI-disclosure governance — run
image-likeness-and-rightsif the asset depicts a real person or brand mark. Attach the AI-generation disclosure footer permedia-governance-routingbefore delivering output.
What ships with it
1 file 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.
- 12d ago First seen · 106 lines · 37 tokens per session scan A 84d2a4a7c6e6
brand-asset-generation is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,292 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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