Discover Testing

Testing Skill Discovery (English)

Chinese version: See the corresponding Chinese skill.

When to Use

• Need to decide which testing skill should be used before execution.
• The request mixes multiple testing directions or phases.

Workflow

1. Read the user request and first identify the capability stage (Core QA / Engineering QA / Production Quality / AI Native QA) and primary testing goal.
2. Follow the routing prompt under prompts/: pick 1 primary skill; add at most 1 helper only when needed.
3. Hand the request to the target skill; do not execute the full testing work inside this router skill.

Core Constraints

• Recommend few skills — avoid menu dumping.
• If the target skill is already obvious, say so directly.
• Make the route actionable: name the skill and the reason.
• Use ai-assisted-testing for AI for QA; Testing for AI belongs to AI Native QA. Until roadmap Skills are installed, never recommend them as callable primary Skills.

Progressive Disclosure

• Before producing output, read and follow prompts/discover-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.
• For step ↔ prompt mapping: read reference.md.

Pre-delivery Checklist

• Followed the main prompt's output structure
• Minimum coverage focus: main goal, best-fit primary skill, optional supporting skill, why this choice fits, next step to continue work (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 recommend many skills at once.
• Do not turn skill selection into full test execution.
• Do not pretend a route is complete when information is insufficient.