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qa-workflow

Orchestrate end-to-end QA workflows with conditional UI and API checks.

Updated Aug 27, 2026
Search Tags:#automation#git#workflow#qa#test-generation#change-analysis#metersphere
One-click install
npx skills add https://github.com/taptap/agents-plugins --skill qa-workflow-taptap
Or copy as Structured Prompt for Agentβ–Ό
Please help me install this Agent Skill.
Skill: qa-workflow
Source: https://github.com/taptap/agents-plugins/tree/main/plugins/test/skills/qa-workflow
Command: npx skills add https://github.com/taptap/agents-plugins --skill qa-workflow-taptap

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating requirement clarification, test-case generation, test plan creation, code verification, and reporting across multiple tools and team gates is time-consuming and error-prone. This Skill automates the orchestration of those steps so teams can run an end-to-end QA process reliably, resume after interruptions, and produce a single QA summary.

Core Features & Use Cases

  • Automated orchestration: Calls downstream test skills in the correct sequence and handles parameter passing between steps.
  • Conditional branching & templates: Supports qa-full, qa-lite, and verify-only templates and runs UI or API checks only when relevant.
  • Parallel verification: Executes change-analysis, verification-test-generation, and ui-fidelity-check in parallel when possible.
  • Multi-source input & code handling: Accepts story links, local requirement documents, or freeform text; supports MR/PR URLs or local git diffs and can create PRs as an optional step.
  • Stateful execution & recovery: Persists workflow_state.json for progress tracking, supports resume, error handling, and generates qa_summary.md as final output.
  • Tool integration: Integrates with MeterSphere for plan sync/execution and with git workflows for review and PR creation.

Quick Start

Run a full QA workflow by providing a story_link or uploading the requirement document to start requirement clarification, test generation, and subsequent verification steps.

Frequently Asked Questions about qa-workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate end-to-end QA workflows from requirement clarification to code verification?β–Ό

You can automate end-to-end QA workflows by triggering the process with a story link, requirement document, or local git diff. The workflow orchestrates test-case generation, MeterSphere synchronization, and final code checks automatically.

Can I use local git diffs or MR URLs for change analysis and test generation?β–Ό

Yes, local git diffs and MR/PR URLs are supported as inputs for change analysis and test generation. The workflow processes these code changes to execute conditional UI and API checks, and optionally integrates with PR creation.

Does MeterSphere support automated test plan synchronization and execution?β–Ό

MeterSphere supports automated test plan synchronization and execution through this workflow. It integrates directly to sync generated test cases, execute plans, and persist the execution state in workflow_state.json for tracking and recovery.

How do I resume an interrupted QA workflow and recover from errors?β–Ό

To resume an interrupted QA workflow, the system persists progress in workflow_state.json. This stateful execution allows you to restart from the last completed step, providing error-recovery behaviors without losing prior test generation or analysis data.

What is the difference between qa-full, qa-lite, and verify-only workflow templates?β–Ό

The qa-full template runs the entire process from requirement clarification to final checks, qa-lite runs a streamlined version, and verify-only skips generation to focus solely on code verification. Templates trigger conditional UI or API checks based on relevance.

What are the limitations of automating test generation with freeform text inputs?β–Ό

When automating test generation with freeform text inputs, the workflow lacks structured story links or requirement documents, which may reduce the precision of change analysis. It still processes the text for test-case generation but relies heavily on the provided context.

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