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 ferdinandobons/brand-docs --skill brand-xlsxgit clone --depth 1 https://github.com/ferdinandobons/brand-docsWrote 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/ferdinandobons/brand-docs/brand-xlsx)<a href="https://agentmods.dev/skills/ferdinandobons/brand-docs/brand-xlsx"><img src="https://agentmods.dev/badge/skills/ferdinandobons/brand-docs/brand-xlsx/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/ferdinandobons/brand-docs/brand-xlsx"><img src="https://agentmods.dev/badge/skills/ferdinandobons/brand-docs/brand-xlsx.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00141 | $0.03138 |
| Opus 5 | $0.00071 | $0.01569 |
| Sonnet 5 | $0.00028 | $0.00628 |
| Haiku 4.5 | $0.00014 | $0.00314 |
Grade A, and why
brand-xlsx 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 10d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
brand-xlsx
Use this skill when the user wants reusable branded Excel/workbook generation
from a company .xlsx template and variable user-provided data.
This is an AI-agent skill for Codex and Claude Code. The user should describe
the workbook/model they want filled; the agent maps that request to named cells
and named regions, invokes the internal Python engine, verifies the output, and
returns the generated .xlsx.
The seven verbs: three deterministic + four model-assisted
Every brand skill (brand-docx, brand-pptx, brand-xlsx) implements the same
contract. The deterministic core is extract / verify / generate; on top of it
sit the optional learning verbs comprehend / learn / propose-overrides /
refine, each fail-closed (the engine validates every proposal and authors every
value).
| Verb | Input | Output |
|---|---|---|
| extract | a company .xlsx template |
a reusable Brand Profile |
| comprehend (optional, model-driven) | a saved profile + a model-authored comprehension.json |
the profile with a validated, cached comprehension block |
| verify | a saved Brand Profile | QA findings + a verdict |
| generate | data (a GridDocument) + a profile | a new on-brand .xlsx |
| learn (deterministic distillation) | the profile's cross-run generation history | recurring QA findings distilled into shell-frozen overrides, advisory until --accept |
| propose-overrides (model-driven) | the recurring remainder learn could not bind + a model-authored proposal |
shell-backed corrections through the same fail-closed sink, advisory until --accept |
| refine | end-of-generation user feedback (text or a screenshot) as a refinement.json delta |
the existing comprehension overlaid for FUTURE generations, advisory until --accept |
comprehend is optional: generate works on the deterministic profile alone.
See reference/comprehension.md for the full step.
Hard Rules
- Treat
python scripts/cli.py ...as an internal engine command, not the user-facing workflow. scripts/cli.pyis a LAUNCHER that locates the engine root by itself: it works from this skill folder AND from the repo/plugin root (setBRAND_DOCS_ROOTto override). Never guess deeper paths likescripts/brandkit/....- Run the dependency preflight before starting extract / comprehend / verify / generate, and report missing or unusable dependencies before proceeding.
- Extract opens the source template read-only and saves
brand-kit/<name>/template/shell.xlsxbyte-for-byte. - Generate opens the saved shell and resolves every named cell/region through
profile.json. - Do not put style names, colors, fonts, or brand identifiers in a GridDocument.
- If the user did not provide a template or enough data, ask for the missing input.
- Return the generated file path plus a QA summary.
- Consult
profile.json.artifact_catalogbefore generation when the user asks to mimic a specific piece of the template.
What ships with it
3 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.
- 10d ago First seen · 246 lines · 141 tokens per session scan A 9804de7306e9
brand-xlsx is a skill published in the GitHub repository ferdinandobons/brand-docs (259 stars, last pushed 2mo ago), licensed MIT. It adds 141 tokens to every session and 3,138 once invoked, about $0.0007 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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