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Quality Engineering Zephyr Test Generation

Community
HoangNguyen0403
quality-engineering-zephyr-test-generation

Generate Zephyr test cases from Jira stories: parse acceptance criteria and business rules, impact-analyze existing TCs (update vs. create new), and draft correctly named test cases. Use for AC-to-test generation; defer post-generation Jira linking and manual test-case quality review.

Overview

PublisherHoangNguyen0403
Repositoryagent-skills-standard
Skill namequality-engineering-zephyr-test-generation
Stars
573
Forks
166
Bundled files
4
LicenseMIT
Links
  • 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.

  • 4 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 Zephyr Test Generation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-zephyr-test-generation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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-zephyr-test-generation .claude/skills/quality-engineering-zephyr-test-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quality Engineering Zephyr Test Generation 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 Zephyr Test Generation 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 Zephyr Test Generation 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.

Zephyr Test Generation Standards

Priority: P1 (HIGH)

Workflow: Jira → Zephyr

  1. Analyze Requirements:
  • Extract: Summary, ACs, Platform per AC row, Market, Components.
  • Fetch Jira with ?expand=renderedFields — HTML authoritative for platform colors: #00B8D9 = Web · #36B37E = Mobile · #FF991F = Web+Mobile
  • See Actor/Permission Matrix for role/market logic.
  • HALT on ambiguous AC per quality-engineering-test-plan-authoring scenario expansion: ultra-short AC, no expected behavior, bundled ACs, contradiction. Ask before drafting.
  1. Impact Analysis (run before any TC creation)
  • Step — Direct Lookup: Call Get Issue Link Test Cases with Jira issue key (e.g., {PROJECT}-{ID}).
  • Step B — Supplemental: If Step 0, search by [Module] and [Screen] keywords + check sibling issue links.
  • See Discovery Protocol for full chain.
  • Map each AC to coverage status:
  • Covered → ask user: skip or update to current format?
  • Partial → always propose NEW TC.
  • Not Covered → always create NEW TC.
  1. Draft Artifact:
  • Delete any existing zephyr_test_plan.md before writing.
  • Follow 4-section format in TC Format Reference exactly.
  • After writing: read back file and print full content in chat so user can review without opening it.
  • Ask for: review approval, handling of Covered ACs, and Zephyr Folder ID.
  1. Create in Zephyr (after explicit user approval)
  • Create Test Case (with customFields included — no separate Update needed) → Create Test Case Steps → Create Test Case Issue Link
  • For updates to existing TCs: fetch current steps via Get Test Case Steps, show before/after diff, wait for explicit approval, then Update Test Case.
  • Param contract per SmartBear API Notes.

Platform Rules

AC rowAction
Single row [ WEB + MOBILE ]ONE TC, Platform = "Web and Mobile", no platform prefix in name
Two rows same behavior, different platformsTWO TCs with Web_ / Mobile_ prefix — never merge

Naming & Filing

  • Name: Prefix Web_ / Mobile_ only when platform-exclusive; omit prefix for Web and Mobile.
  • Folder: Use exact Folder ID provided by user or specified in Technical Impact.

Role Mapping Rule

  • CRITICAL: If Acceptance Criteria uses generic terms like "user", "buyer", or "customer" in ordering/checkout context, it MUST mapped to ALL purchasing roles: ["Client user", "Client admin", "Internal sales rep", "External sales rep"]. not default to Client user.

Anti-Patterns

  • No prefix omission: TC name sent to Zephyr API must include Web_ or Mobile_ prefix for platform-exclusive TCs — copy verbatim from artifact draft; omit prefix only when Platform = "Web and Mobile".
  • No Draft skip: Always set status = Draft; never automatically approve.
  • No flat folderId: Use "folder": {"id": X} in all PUT payloads.
  • No WEB+MOBILE split: One AC row = one TC with Platform "Web and Mobile".
  • No platform merge: Two AC rows, different platforms = two separate TCs.
  • No silent update: Show before/after diff; wait for explicit approval.
  • No lookup skip: Always run Step direct link lookup before supplemental search.
  • No stale artifact: Delete existing zephyr_test_plan.md before each run.
  • No coverage skip: Coverage Analysis table must open every artifact.
  • No ghost update: Update Zephyr TC whenever matching code changes.
  • No vague steps: Use specific observable outcomes — e.g., "System works" → "Banner 'Success' is visible".
  • No invented expected result: cite the AC or business rule, or tag ASSUMED and surface it for review.
  • No rephrased negatives: N and E scenarios must change a precondition or input, not the wording.
  • No generation on ambiguous AC: HALT and ask; a convincing TC must not hide a missing requirement.

Coverage wording

  • During impact analysis, map each AC to Covered, Partial, or Not Covered and check for duplicate test cases before drafting new ones.

Canonical response anchors

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

  • duplicate

  • impact analysis

  • no platform prefix

  • separate

  • Additional task-grounded exact anchors: Issue Link

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 Zephyr Test Generation AI skill do?

Generate Zephyr test cases from Jira stories: parse acceptance criteria and business rules, impact-analyze existing TCs (update vs. create new), and draft correctly named test cases. Use for AC-to-test generation; defer post-generation Jira linking and manual test-case quality review.

Why use Quality Engineering Zephyr Test Generation on TypingMind?

Because you install it once and use it with any model. Quality Engineering Zephyr Test Generation 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 Zephyr Test Generation 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-zephyr-test-generation. 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 Zephyr Test Generation?

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 Zephyr Test Generation?

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

Is the Quality Engineering Zephyr Test Generation 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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