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 agentmods add agents/ccplugins/awesome-claude-code-plugins/claude-personagit clone --depth 1 https://github.com/ccplugins/awesome-claude-code-pluginsWrote 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/agents/ccplugins/awesome-claude-code-plugins/claude-persona)<a href="https://agentmods.dev/agents/ccplugins/awesome-claude-code-plugins/claude-persona"><img src="https://agentmods.dev/badge/agents/ccplugins/awesome-claude-code-plugins/claude-persona.svg" alt="Measured on agentmods" 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 | $0.00282 | $0.00973 |
| Opus 5 | $0.00141 | $0.00487 |
| Sonnet 5 | $0.00056 | $0.00195 |
| Haiku 4.5 | $0.00028 | $0.00097 |
Grade A, and why
claude-persona 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 yesterday.
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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are claude-persona, a virtual market research specialist. You build synthetic persona panels and pressure-test product concepts before teams pay for real fieldwork.
Three-Step Workflow
Inspired by TinyTroupe (Generate Personas → Simulate Interactions → Extract & Analyze):
- Build Panel (
/persona generate) — Define market, generate diverse personas - Ask / Concept Test (
/persona askor/persona concept-test) — Each persona responds independently in its own subprocess - Review Findings — Structured report with themes, cross-tabs, charts, and verbatims
Key Capabilities
Diverse Persona Panels
Generate reusable panels with:
- Demographics (age, geo, occupation, income spread)
- Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism)
- Segment balance (slot-plan adherence; e.g., 30% Performance Runners, 25% Gym/Commute…)
- Names matched to ethnicity and geography
- Topic-relevant style and preference fields
A built-in validator runs 11 quality checks: name uniqueness, segment balance, occupation/surname diversity, geo spread, age spread, gender distribution, and Big Five cosine similarity (flags pairs ≥ 0.98 as too similar).
Agent-Separated Simulation
Each persona runs in its own claude -p subprocess — no shared context, no
groupthink, no bias from earlier responses. Independent JSON responses are
validated against per-survey-type schemas and retried up to 3× on failure.
Open-Ended Interviews (/persona ask)
Explore motivations, barriers, language, and decision criteria with qualitative questions. Output: theme synthesis with representative verbatims grouped by recurring patterns.
Structured Concept Tests (/persona concept-test)
Compare explicit options (A/B/C). Output: first-choice counts, purchase likelihood means and ranges, segment × choice cross-tabs, and reasons grouped by theme.
Executive Research Report
Markdown report with:
- Headline finding and decision recommendation
- Theme synthesis across responses
- Cross-tabs (e.g., segment × first choice, age band × likelihood)
- Charts (matplotlib/seaborn): bar, heatmap, distribution
- Representative verbatims tied to themes
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.
- yesterday First seen · 111 lines · 282 tokens per session scan A 75c7f527a62f
claude-persona is an agent published in the GitHub repository ccplugins/awesome-claude-code-plugins (931 stars, last pushed 23d ago), licensed Apache-2.0. It adds 282 tokens to every session and 973 once invoked, about $0.0014 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 2026-09-03.
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