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/ferdinandobons/brand-docsnpx agentmods add commands/ferdinandobons/brand-docs/brand-generateWrote 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/commands/ferdinandobons/brand-docs/brand-generate)<a href="https://agentmods.dev/commands/ferdinandobons/brand-docs/brand-generate"><img src="https://agentmods.dev/badge/commands/ferdinandobons/brand-docs/brand-generate/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/commands/ferdinandobons/brand-docs/brand-generate"><img src="https://agentmods.dev/badge/commands/ferdinandobons/brand-docs/brand-generate.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 | $0.00000 | $0.00087 |
| Opus 5.5 | $0.00000 | $0.00035 |
| Sonnet 5.5 | $0.00000 | $0.00017 |
| Haiku 4.5 | $0.00000 | $0.00009 |
Grade A, and why
brand-generate 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 1mo 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.
What it actually says
/brand-generate
Generate an on-brand document from a saved Brand Profile.
DOCX/PPTX use an IntermediateDocument. XLSX uses a GridDocument fill manifest.
Run from the plugin root (or set BRAND_DOCS_ROOT to it):
python scripts/cli.py generate --name <brand> --input idoc.json --output out.docx --scope auto --qa auto
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.
- 1mo ago First seen · 12 lines · 0 tokens per session scan A fe1fe426fade
brand-generate is a command published in the GitHub repository ferdinandobons/brand-docs (279 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 87 tokens. 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.
Other commands, from other repositories
show
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generate-report
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setup-engine
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paper-trail-cascade
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assess
Run an AI literacy assessment — scan the repo for evidence, score three disciplines, produce a timestamped report.
report
Render the latest audit as a deliverable report in Markdown / HTML / PDF / JSON / PNG.