AI-Assisted Testing - QA Skill Library

Why this Skill

It turns this Skill's method into a quality input that can be executed, reviewed, and reused.

Need an output that can be used directly for execution, review, or follow-up.
Prioritize by risk / business impact — do not treat everything equally.
Separate confirmed facts from current assumptions.
Do not invent endpoints, fields, environments, or root causes the user did not provide.
Do not pretend completeness when scope/context is missing.

When to use

Use this Skill

Need help with ai assisted testing in a real project context.
Need an output that can be used directly for execution, review, or follow-up.

Common pitfalls

Do not pretend completeness when scope/context is missing.
Do not treat every item as equally important.
Do not skip assumptions and information gaps.
Do not dump generic theory unrelated to the current toolchain.

Input

Minimum Input

The current task scope, objective, and subject under review.

Recommended Input

Before producing output, read and follow prompts/ai-assisted-testing.md (minimum coverage, output structure, quality bar).
When Excel/CSV/JSON/Word is requested: read output-formats.md and honor the format.
When a ready-made template fits: use matching files under output-templates/.
For format conversion or helper checks: prefer existing scripts/ over reinventing.

Optional Context

For evaluating/regressing this skill: use evals/ with skill-up.

Output

The output follows this Skill's method and makes facts, assumptions, risks, and next steps explicit.

Judge the output value before installing

01Followed the main prompt's output structure
02Minimum coverage focus: task scope, best AI-assisted opportunities, human verification points, high-risk areas that need manual judgment, draft artifacts to generate, review and approval steps, quality gates, time-saving opportunities, ... (details in main prompt)
03Covered the minimum checklist, or explained omissions
04High-risk items have explicit priority

View full output structure

05Did not invent details the user did not provide
06Assumptions and gaps are marked

How It Works

01Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
02Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
03If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
04Default to Markdown; switch formats only when the user asks.

Install & Quick Start

Install command / SHELL

npx skills add \
  https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-assisted-testing
  -g

ai-assisted-testing.prompt

@skill ai-assisted-testing

Using the current project context, produce an actionable result following this Skill.

Additional context:
[Paste project context or requirement]

---
name: ai-assisted-testing
description: Use this skill when you need AI-assisted testing workflows such as test data generation, root-cause analysis, and prioritization; triggers include AI-assisted testing and AI for QA.
---

# AI-Assisted Testing

**Chinese version：** See the corresponding Chinese skill.

## When to Use

- Need help with ai assisted testing in a real project context.
- Need an output that can be used directly for execution, review, or follow-up.

## Workflow

1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
2. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
3. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
4. Default to Markdown; switch formats only when the user asks.

## Core Constraints

- Prioritize by risk / business impact — do not treat everything equally.
- Separate confirmed facts from current assumptions.
- Do not invent endpoints, fields, environments, or root causes the user did not provide.
- Keep output executable: concrete scenarios, clear priority, clear next steps.

## Progressive Disclosure

- Before producing output, read and follow `prompts/ai-assisted-testing.md` (minimum coverage, output structure, quality bar).
- When Excel/CSV/JSON/Word is requested: read `output-formats.md` and honor the format.
- When a ready-made template fits: use matching files under `output-templates/`.
- For format conversion or helper checks: prefer existing `scripts/` over reinventing.
- For evaluating/regressing this skill: use `evals/` with skill-up.

## Pre-delivery Checklist

- [ ] Followed the main prompt's output structure
- [ ] Minimum coverage focus: task scope, best AI-assisted opportunities, human verification points, high-risk areas that need manual judgment, draft artifacts to generate, review and approval steps, quality gates, time-saving opportunities, ... (details in main prompt)
- [ ] Covered the minimum checklist, or explained omissions
- [ ] High-risk items have explicit priority
- [ ] Did not invent details the user did not provide
- [ ] Assumptions and gaps are marked

## Common Pitfalls

- Do not pretend completeness when scope/context is missing.
- Do not treat every item as equally important.
- Do not skip assumptions and information gaps.
- Do not dump generic theory unrelated to the current toolchain.