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 Orkas-AI/Orkas-Awesome-AgentSkills --skill product-testgit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/product-test)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/product-test"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/product-test/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/orkas-ai/orkas-awesome-agentskills/product-test"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/product-test.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 | $0.00074 | $0.00839 |
| Opus 5 | $0.00037 | $0.00419 |
| Sonnet 5 | $0.00015 | $0.00168 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
product-test 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 9d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Test
Use this skill to define the observable behavior that must be true for a story or feature to be considered done. It turns product context into concise, testable Given/When/Then scenarios that engineers and QA can verify without guessing intent.
Do not use this skill to write a full PRD, redesign the feature, break work into engineering tasks, write implementation plans, run tests, write automation code, or create exhaustive test suites. Stay focused on product-level acceptance scenarios for a specific story, feature slice, or behavior.
When To Use
- A user story, PRD section, or feature slice already exists.
- The team needs clear pass/fail conditions before or during implementation.
- QA needs explicit happy path, edge case, error state, or non-functional scenarios.
- A story is too vague and needs observable done conditions.
If the feature scope is unclear, ask for the smallest missing context before drafting criteria.
Instructions
- Confirm the slice: identify the exact story, workflow, user role, or behavior being accepted.
- Capture inputs and assumptions: list source material, scope boundaries, and missing context.
- Separate scenario types: cover happy path first, then edge cases, error states, and non-functional expectations.
- Use Given/When/Then only: each criterion should be independently testable and observable.
- Describe recovery behavior: when validation fails or a dependency breaks, state what the user sees and how they can recover.
- Avoid implementation leakage: do not mention internal classes, tables, functions, or code paths unless the acceptance surface is technical by nature.
- Review for pass/fail clarity: rewrite subjective criteria into measurable outcomes.
- End with handoff: state what is ready for QA or the implementation team and what still needs clarification.
Output Format
Use references/product-test-template.md as the default structure. Keep scenarios concise and avoid duplicate coverage.
What ships with it
1 file 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.
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.
- 9d ago First seen · 58 lines · 74 tokens per session scan A 5d28964f36db
product-test is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 839 once invoked, about $0.0004 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.
Other skills, from other repositories
test-reporting
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
Vizra ADK Evaluation Framework
Test and evaluate AI agents with automated evaluations, assertions, and LLM-as-a-Judge patterns.
auto-qa
QAMESH project QA mesh — plan, run, report, and publish deterministic QA evidence.
auto-canary
A deployment health-check skill for testing whether a recently deployed or staging website is working. It runs build, end-to-end, and browser checks, then reports a pass, warning, or failure.
tdd
A Test-Driven Development guide. TDD means writing a failing test first, adding the smallest implementation that passes it, and then improving the code while keeping the tests passing.
accessibility-a11y
WCAG 2.2 compliance, ARIA patterns, keyboard navigation, screen readers, automated testing.