Quality Engineering Quality Assurance

Community

HoangNguyen0403

quality-engineering-quality-assurance

Write or review manual Zephyr test cases with 1-condition-per-TC granularity, Module_Action on Screen when Condition naming, platform prefix rules, and High/Normal/Low priority classification. Use for test-case authoring and review; defer Jira traceability, linking, and pushing cases to Zephyr.

Overview

Publisher	HoangNguyen0403
Repository	`agent-skills-standard`
Skill name	`quality-engineering-quality-assurance`
Stars	573
Forks	166
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by HoangNguyen0403 on GitHub. Read the source before you install it.

Installation

Install the Quality Engineering Quality Assurance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-quality-assurance
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-quality-assurance

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standard.git /tmp/agent-skills-standard
mkdir -p .claude/skills
cp -r /tmp/agent-skills-standard/.agents/skills/quality-engineering/quality-engineering-quality-assurance .claude/skills/quality-engineering-quality-assurance

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quality Engineering Quality Assurance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Quality Engineering Quality Assurance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Quality Engineering Quality Assurance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Quality Assurance Standards

Priority: P1 (HIGH)

1. Test Case Granularity

1 Test Case = 1 Condition on 1 Screen.
Split Screens: "Order Details" & "Item Details" separate.
Split Conditions: "Config " & "Config B" separate.
No "OR" Logic: Each TC must test single, distinct path.

2. Naming Convention

Pattern: Platform_Module_Action on Screen when Condition (e.g., Web_Order_Verify... or Mobile_Order_Verify...)
Rule: Only include Web_ or Mobile_ prefix if requirement exclusive to one platform. Omit prefix if it supports Both.
Example: Order_Verify payment term on Item Details when Toggle is OFF (Supports Both)

3. Priority Levels

Use priority rationale to justify each classification:

High: Critical path, blocker bug.
Normal: Standard validation, edge case.
Low: Cosmetic, minor improvement.

4. References

Detailed Examples

Anti-Patterns

No Broad TCs: "Verify order flow works" — too broad; every TC must cover exactly 1 condition on 1 screen
No Shared TCs (Divergent): Testing Web and Mobile behavior in single TC when behavior diverges — split into separate TCs per platform
No Incomplete Naming: Order_Verify page — name must follow full pattern: Module_Action on Screen when Condition
No Priority Inflation: Marking cosmetic spacing bug as High priority — reserve High for critical path blockers only

Review wording

Call out naming violations explicitly when a test case breaks the naming convention, then split it into separate TCs with one condition on one screen.

Canonical response anchors

When this skill applies, preserve the following domain terminology or equivalent concrete examples in the answer when relevant:

naming violations
1 Test Case = 1 Condition
High: Critical path
Low: Cosmetic
Module_Action on Screen when Condition
No "OR" Logic
Split into separate TCs
priority rationale

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Quality Engineering Quality Assurance AI skill do?

Why use Quality Engineering Quality Assurance on TypingMind?

Because you install it once and use it with any model. Quality Engineering Quality Assurance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Quality Engineering Quality Assurance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-quality-assurance. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Quality Engineering Quality Assurance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Quality Engineering Quality Assurance?

As many as you like. As long as a model supports skills, you can use Quality Engineering Quality Assurance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Quality Engineering Quality Assurance AI skill free?

Yes. It is published on GitHub by HoangNguyen0403 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Quality Assurance Standards

Priority: P1 (HIGH)

1. Test Case Granularity

2. Naming Convention

3. Priority Levels

4. References

Anti-Patterns

Review wording

Canonical response anchors

Bundled files

Frequently asked questions

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