ai-assisted-testing

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

中文版： 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.