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 vasilyu1983/AI-Agents-public --skill qa-persona-testinggit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/qa-persona-testing)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/qa-persona-testing"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/qa-persona-testing/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/vasilyu1983/ai-agents-public/qa-persona-testing"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/qa-persona-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.02786 |
| Opus 5 | $0.00018 | $0.01393 |
| Sonnet 5 | $0.00007 | $0.00557 |
| Haiku 4.5 | $0.00004 | $0.00279 |
Grade A, and why
qa-persona-testing 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 7d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Persona Testing
Test a solution or app as a specific person from a specific group — an Ideal Customer Profile (ICP) or defined user segment — rather than as a QA engineer. The skill covers the full loop: find the profile (using sibling skills), set the persona up as an executable specification, drive the app through browser automation like that person would, and produce an improvement report with what to fix, what to avoid, and what to keep.
This is persona simulation, not E2E test authoring. It finds friction, confusion, trust breaks, and abandonment; it does not replace assertion-based regression tests (qa-testing-playwright) or research with real users (software-ux-research).
Quick Reference
| Task | Read or use | Outcome |
|---|---|---|
| Find/derive the ICP or persona group | ## Phase 1 — DEFINE + sibling skills table |
Evidence-backed persona candidates |
| Turn a persona into an executable spec | assets/persona-profile.md + references/persona-construction.md | Persona profile with behavioral traits and scenario pack |
| Run a browser test session as the persona | references/browser-execution.md | Filled assets/session-log.md per scenario |
| Keep the simulation honest (anti-sycophancy) | references/persona-construction.md | Persona that abandons, complains, and stays in character |
| Write the improvement report | assets/findings-report.md + references/reporting-and-validity.md | Ranked findings, prioritized fixes, validity caveats |
| Decide what simulation can and cannot claim | references/reporting-and-validity.md | Correctly-hedged conclusions |
When to Use
- "Test this app like a {{first-time user / busy accountant / 60-year-old iPad user / our ICP}}"
- "Would our target customer actually get through onboarding?"
- "Run a synthetic user / persona-based walkthrough and tell me what to improve"
- Pre-launch UX sanity pass when real-user testing is not yet feasible
- Regression of a redesigned flow against the personas that matter commercially
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 333 B
- assets/findings-report.md 2.1 KB
- assets/persona-profile.md 2.8 KB
- assets/session-log.md 2.1 KB
- data/sources.json 8.6 KB
- learnings.consolidated.md 593 B
- learnings.md 186 B
- references/browser-execution.md 9.8 KB
- references/persona-construction.md 11 KB
- references/reporting-and-validity.md 6.5 KB
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.
- 7d ago First seen · 154 lines · 36 tokens per session scan A 3849585816b7
qa-persona-testing is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 9d ago), licensed MIT. It adds 36 tokens to every session and 2,786 once invoked, about $0.0002 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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