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 brainbytes-dev/everything-claude-marketing --skill persona-buildinggit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWrote 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/brainbytes-dev/everything-claude-marketing/persona-building)<a href="https://agentmods.dev/skills/brainbytes-dev/everything-claude-marketing/persona-building"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/persona-building/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/brainbytes-dev/everything-claude-marketing/persona-building"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/persona-building.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 | $0.00028 | $0.02840 |
| Opus 5.5 | $0.00011 | $0.01136 |
| Sonnet 5.5 | $0.00006 | $0.00568 |
| Haiku 4.5 | $0.00003 | $0.00284 |
Grade A, and why
persona-building 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 2mo 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona Building
When to Activate
Use this skill when creating buyer personas for a new product or market, refining existing personas with fresh data, building audience segments for campaigns, defining ideal customer profiles (ICP) for sales alignment, developing messaging architecture by audience, or when someone asks to "define our target audience" or "create personas." Also activate when marketing feels unfocused or messaging is trying to speak to everyone.
First Questions
Before building personas, clarify:
- B2B or B2C? (changes the persona structure significantly)
- How many personas are needed? (aim for 2-4 primary personas — more than that dilutes focus)
- What data sources exist? (customer interviews, CRM data, analytics, surveys, sales feedback)
- Are there existing personas? (audit before rebuilding — they may need updating, not replacing)
- What will these personas be used for? (messaging, content strategy, product development, ad targeting, sales enablement)
- Who are the internal stakeholders? (marketing, sales, product, leadership — personas must be shared and adopted)
Core Rules
- Personas are based on data, not imagination. A persona built from assumptions is fiction. Every attribute should trace back to research — interviews, surveys, analytics, or sales data.
- Personas describe behavior patterns, not demographics. Demographics are descriptive but not predictive. What someone does, what they want, and what prevents them from getting it — these drive marketing decisions.
- Fewer personas, deeper understanding. Two well-researched personas beat six shallow ones. If you can't describe how their buying behavior differs, they may be the same persona.
- Personas are living documents. Update them quarterly or whenever significant new data emerges. Stale personas lead to stale marketing.
- Anti-personas are as important as personas. Knowing who NOT to target prevents wasted budget and misaligned messaging.
- Jobs-to-be-Done is the foundation. Demographics tell you who someone is. JTBD tells you why they buy. Lead with jobs.
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.
- 2mo ago First seen · 233 lines · 28 tokens per session scan A 4817facb65c0
persona-building is a skill published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 28 tokens to every session and 2,840 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
persona-building compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| persona-buildingbrainbytes-dev/everything-claude-marketing | Skill | 10 | 7mo ago | MIT | A | 28 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |