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/robroyhobbs/marketing-skillsnpx agentmods add skills/robroyhobbs/marketing-skills/brand-voiceWrote 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/robroyhobbs/marketing-skills/brand-voice)<a href="https://agentmods.dev/skills/robroyhobbs/marketing-skills/brand-voice"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/brand-voice/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/robroyhobbs/marketing-skills/brand-voice"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/brand-voice.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 | $0.00145 | $0.11769 |
| Opus 5 | $0.00072 | $0.05885 |
| Sonnet 5 | $0.00029 | $0.02354 |
| Haiku 4.5 | $0.00015 | $0.01177 |
Grade A, and why
brand-voice 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 — 1,502 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/brand-voice — Brand Voice Engine
Generic copy converts worse than copy with a distinct voice. Not because the words are different — because the reader feels like they're hearing from a PERSON, not a marketing team.
This skill defines that voice. Either by extracting it from existing content, building it strategically from scratch, or auto-scraping a URL to analyze a brand's public presence.
Read ./brand/ per _system/brand-memory.md
Follow all output formatting rules from _system/output-format.md
Brand Memory Integration
On every invocation, check for existing brand context:
Reads (if they exist)
| File | What it provides | How it shapes output |
|---|---|---|
| ./brand/positioning.md | Market angles, differentiators | Informs voice positioning — a rebel brand sounds different from a trusted advisor |
| ./brand/audience.md | Buyer profiles, sophistication level | Jargon tolerance, formality level, cultural references |
Writes
| File | What it contains |
|---|---|
| ./brand/voice-profile.md | The complete voice profile (markdown + embedded JSON) |
Context Loading Behavior
- Check whether
./brand/exists. - If it exists, read
positioning.mdandaudience.mdif present. - If loaded, show the user what you found:
Brand context loaded: ├── Positioning ✓ "{primary angle summary}" └── Audience ✓ "{audience summary}" Using this to shape your voice profile. - If files are missing, proceed without them. Note at the end:
→ /positioning-angles would sharpen this profile → /audience-research would tune jargon level
Iteration Detection
Before starting any mode, check whether ./brand/voice-profile.md already
exists.
If voice-profile.md EXISTS → Update Mode
Do not start from scratch. Instead:
- Read the existing profile.
- Present a summary of the current voice:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ EXISTING VOICE PROFILE Last updated {date} by {skill} ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Voice summary: {current summary} Tone spectrum: ├── Formal ↔ Casual: {position} ├── Serious ↔ Playful: {position} ├── Reserved ↔ Bold: {position} ├── Simple ↔ Sophisticated: {position} └── Warm ↔ Direct: {position} ────────────────────────────────────────────── What would you like to change? ① Refine the tone (adjust the spectrum) ② Update vocabulary (add/remove words) ③ Add new content samples (re-extract) ④ Full rebuild (start from scratch) ⑤ Auto-scrape my site for fresh data
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 · 1,502 lines · 145 tokens per session scan A 53b46f5e4229
brand-voice is a skill published in the GitHub repository robroyhobbs/marketing-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 145 tokens to every session and 11,769 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…