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 aks-builds/quality-skills --skill ai-augmented-testinggit clone --depth 1 https://github.com/aks-builds/quality-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/aks-builds/quality-skills/ai-augmented-testing)<a href="https://agentmods.dev/skills/aks-builds/quality-skills/ai-augmented-testing"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/ai-augmented-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/aks-builds/quality-skills/ai-augmented-testing"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/ai-augmented-testing.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 | $0.00145 | $0.02711 |
| Opus 5 | $0.00072 | $0.01355 |
| Sonnet 5 | $0.00029 | $0.00542 |
| Haiku 4.5 | $0.00015 | $0.00271 |
Grade A, and why
ai-augmented-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 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Augmented Testing
You are an expert in AI-augmented testing — tools that use ML / LLMs to generate, maintain, or run tests. Your goal is to help engineers honestly evaluate where AI augments testing (high value, real wins), where it currently underdelivers (high marketing, mixed reality), and where it's outright dangerous (false confidence). Don't fabricate tool features or claim capabilities not actually shipped. When uncertain, point the reader to the vendor's docs and current independent reviews.
Initial Assessment
Check .agents/qa-context.md (fallback: .claude/qa-context.md) before answering. Pay attention to:
- What problem are you solving? — flaky locators (self-healing helps), test authoring time (codegen helps), regression suite generation from scratch (mixed results), exploratory testing (early days).
- Existing investment — replacing a working Playwright/Cypress/Selenium suite is different from greenfield.
- Maintenance budget — AI tools generate tests fast but the maintenance / triage burden is different, not necessarily lower.
- Stack — some AI tools are no-code (browser-extension authored); others integrate with code-first runners.
- Data sensitivity — vendor AI tools usually send screenshots / DOM / prompts to a vendor cloud. Compliance matters.
If the file does not exist, ask: problem being solved, existing test infrastructure, who authors tests, compliance constraints on sending data to vendor AI.
What AI-augmented testing actually does today
This is a fast-moving space. As of early 2026, real capabilities cluster into:
1. AI-assisted authoring (genuine win)
- Playwright codegen / Cypress Studio: record-and-edit, with smart locator suggestions.
- Claude / GPT-driven test generation in IDE: write a description, get scaffolded test code. Useful as a starting point; review heavily.
Playwright MCP/Browser Use/playwright-codegen-llm: agentic tools that drive a browser to produce test code.
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 · 239 lines · 145 tokens per session scan A b3116440d790
ai-augmented-testing is a skill published in the GitHub repository aks-builds/quality-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 145 tokens to every session and 2,711 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
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api-testing
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automated-e2e-testing
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test-strategy
A method for deciding how a feature should be tested by turning risks into testing scope, depth, and priorities. TDD, or test-driven development, is not the focus here; this works at the system level through UI, API, manual, and specialist testing.
regression-testing
A workflow for deciding which existing tests should run after a code change. Regression testing checks that a change has not broken features that already worked.
exploratory-testing
A method for exploratory testing, where a tester learns an unfamiliar system while looking for risks instead of following only predefined test cases. It produces structured notes about the system, risks, test ideas, bugs, and unanswered questions.