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 Ootto-AI/claude-content-skills --skill audience-personasgit clone --depth 1 https://github.com/Ootto-AI/claude-content-skillsWrote 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/ootto-ai/claude-content-skills/audience-personas)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/audience-personas"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/audience-personas/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/ootto-ai/claude-content-skills/audience-personas"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/audience-personas.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 | $0.00023 | $0.00446 |
| Opus 5 | $0.00012 | $0.00223 |
| Sonnet 5 | $0.00005 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
audience-personas 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.
What it actually says
Audience Personas
Turn supplied customer evidence into grounded audience segments. Use it when a marketer has interviews, comments, reviews, or CRM notes and needs to decide who a message is for. It is not for inventing demographics, market size, or personas from intuition.
1. Establish the evidence
Ask for the source, date range, decision owner, offer, and desired audience action. Keep direct quotes separate from summaries and name material that is missing.
2. Group observable patterns
Cluster the evidence by job-to-be-done, desired outcome, objection, exact language, and trigger. Cite the source for each cluster. Do not manufacture a segment just to reach a round number.
3. Produce a usable segment
For each evidence-backed segment, return its job, language, objections, useful message angles, and the question it is already asking. Label confidence and unresolved questions.
4. Hold claims for review
Flag any demographic statement, outcome claim, or customer quote that needs approval before public use.
Hard rules
- Do not infer demographics, income, identity, or intent not present in the source.
- Do not turn one loud comment into a market-wide claim.
- Keep observed language distinct from suggested copy.
- If evidence is thin, ask for more comments, reviews, or interviews.
Failure modes
| Symptom | Cause | Fix |
|---|---|---|
| Generic personas | source has no concrete language | ask for verbatim comments or interviews |
| False certainty | inference appears as fact | label it as a hypothesis |
| Too many segments | minor differences treated as groups | merge around the shared job-to-be-done |
Where it sits
social-listening gathers recurring conversation → audience-personas groups it → positioning-audit turns it into a message.
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 · 47 lines · 23 tokens per session scan A 8c04c6b4f613
audience-personas is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 20d ago), licensed MIT. It adds 23 tokens to every session and 446 once invoked, about $0.0001 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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