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/PicsArt/gen-ai-skillsnpx agentmods add skills/picsart/gen-ai-skills/enterprise-brand-governorWrote 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/picsart/gen-ai-skills/enterprise-brand-governor)<a href="https://agentmods.dev/skills/picsart/gen-ai-skills/enterprise-brand-governor"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-brand-governor/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/picsart/gen-ai-skills/enterprise-brand-governor"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-brand-governor.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 | $0.00015 | $0.01947 |
| Opus 5 | $0.00008 | $0.00974 |
| Sonnet 5 | $0.00003 | $0.00389 |
| Haiku 4.5 | $0.00002 | $0.00195 |
Grade A, and why
enterprise-brand-governor 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Brand Governor
Policy-as-code for AI-generated imagery. Every prompt is pre-validated against brand.md, every output is post-checked, violations escalate to a human approver, and every decision is logged. Built for regulated industries and any enterprise where an off-brand asset in production is a material risk.
When to Use
- Multiple teams (marketing, product, sales, agency partners) generating on the same brand system
- Regulated industries (pharma, finance, alcohol, kids) where imagery has legal constraints
- Brand-safety SLA — zero tolerance for competitor logos, restricted props, or off-palette output reaching production
- Agency handoff — external vendor generating on your brand, you need a gate you control
- Pre-production review cycle needs automation; humans only review escalations
Do not use for: quick exploration / mood-board work (gating slows ideation), or accounts without a written brand system yet (build brand.md first).
Prerequisites
Before rolling the governor across teams:
- Brand system location — path / repo / URL for
brand.md. Who owns it? What's the change-control process? - Policy strictness — reject (halt), flag (log + allow), or tier by asset destination (production = reject, internal = flag)?
- Approval chain — who reviews flagged items? What's the SLA for escalation turnaround (1h, 24h, 3 business days)?
- Logging destination — local
~/.gen-ai/audit/, S3 bucket, or ship to SIEM (Splunk, Datadog)? - Compliance constraints — GDPR / HIPAA / COPPA / financial-services rules that must be encoded in
brand.md? - Rollback plan — if the governor blocks a legitimate launch, who has override authority and how is that logged?
How to Run
The governor runs at three checkpoints: prompt, generation, output.
- Author
brand.md— palette, typography, allowed/denied props, imagery style, voice, regulated-category rules. Versioned in git. Commit SHA is the policy ID. - Pre-flight (prompt lint) —
gen-ai validateagainst the prompt before spending credits. Catches banned terms, disallowed concepts, missing required elements (e.g., disclaimer placement). - Brand-context generation — every
gen-ai generateandgen-ai batch runprompt includes the relevantbrand.mdconstraints. Review violations during QA. - Post-flight (output check) — for critical assets, a second-pass model (
gemini-3-pro-imageor vision check) verifies the output matches policy. Palette sampling, logo presence detection, prop allow-list. - Escalation — any
violationstatus routes to the approver queue. Humans review, approve or reject, decision is logged against the audit ID. - Audit export — daily / weekly export of all decisions to the configured SIEM or compliance archive.
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 · 174 lines · 15 tokens per session scan A ba9b2c9acc41
enterprise-brand-governor is a skill published in the GitHub repository PicsArt/gen-ai-skills (4 stars, last pushed 15d ago), licensed MIT. It adds 15 tokens to every session and 1,947 once invoked, about $0.0001 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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