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- Qa Test Planner
qa-test-planner_skill
- Python
273
GitHub Stars
2
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
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npx veilstrat add skill softaworks/agent-toolkit --skill qa-test-planner- README.md10.6 KB
- SKILL.md18.5 KB
Overview
This skill generates comprehensive test plans, manual test cases, regression suites, Figma-based design validations, and structured bug reports for QA engineers. It delivers actionable artifacts that speed test execution, improve reproducibility, and reduce release risk. Use explicit activation (call the skill by name) to produce tailored QA deliverables.
How this skill works
The skill analyzes feature descriptions or design links, identifies required test types and scope, and applies proven templates to produce structured deliverables. It can extract component specs from Figma MCP URLs, compare implementation against design tokens, and generate discrepancy lists and bug reports. Outputs include prioritized test cases, execution order for regression suites, and reproducible bug reports with evidence guidance.
When to use it
- Preparing test coverage for a new feature or release
- Converting Figma designs into visual validation checks
- Building or refreshing a regression test suite after changes
- Drafting clear, reproducible bug reports for developers
- Creating test execution plans for staging or release validation
Best practices
- Provide requirement text, acceptance criteria, or Figma URLs for precise results
- Specify target platforms, browsers, devices, and test data needs up front
- Prioritize P0/P1 paths to keep regression suites focused and fast
- Include screenshots, logs, and build identifiers when creating bug reports
- Run smoke tests first; block release on any P0 failures
Example use cases
- Generate a test plan and timeline for the user authentication feature
- Create 10 manual test cases for the checkout flow with preconditions and test data
- Build a targeted regression suite for the payment module after a patch
- Compare a login page implementation against a Figma design URL and list mismatches
- Produce a bug report template populated from an observed form validation failure
FAQ
Provide a short feature description, acceptance criteria, environment details, and any Figma URLs. The more precise the inputs (platforms, browsers, test data), the more actionable the output.
Can it check visual differences automatically from Figma?
It extracts design specs via Figma MCP and creates a component-by-component validation checklist; visual diffing still requires screenshots from the implementation or an automated visual-regression tool.