No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ferdinandobons · Claude Skill · ★ 243
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is brand-docs safe to install? View the security audit →
BrandDocs: AI On-Brand Document Generator for Word, PowerPoint & Excel BrandDocs is a set of agent skills that learn your existing Word, PowerPoint and Excel templates and generate new on-brand documents from them. Unlike generic AI document generators, it preserves brand, structure, styles and formulas by construction. Built for Claude Code, Codex and compatible AI agents. [](#project-st
| Stars | 243 |
| Forks | 30 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 72.6987207184264/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-10 |
| Created | 2026-06-05 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
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brand-docs is BrandDocs is a set of agent skills that learn your existing Word, PowerPoint and Excel templates and generate new on-brand documents from them. Unlike generic AI document generators, it preserves bran. It is categorized as a Claude Skill with 243 GitHub stars.
brand-docs is primarily written in Python. It covers topics such as ai-document-generation, claude-code-plugin, codex.
You can find installation instructions and usage details in the brand-docs GitHub repository at github.com/ferdinandobons/brand-docs. The project has 243 stars and 30 forks, indicating an active community.
brand-docs is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to brand-docs on Agent Skills Hub include officecli, mckinsey-pptx, thesis-defense-pptx-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: