pairwise-testing git:20260915.3de5d7aA

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---
name: pairwise-testing
description: Use this skill when you need to identify interactions that need at least pairwise coverage after factors, values, and constraints are explicit; triggers include 成对测试 and pairwise test design.
---

# Pairwise Test Design

identify interactions that need at least pairwise coverage after factors, values, and constraints are explicit. Produce PWT-## findings. This Skill organizes traceable test-design candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.

## When to Use

- When you need Pairwise Test Design candidates from test factors, values for each factor, combination constraints, platform/role dimensions, risk evidence, and existing combinations.
- When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action.
- When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries.

Do not use it to execute tests, invent rules, replace a complete strategy, or accept risk for a Human.

## Output Format Options

- Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
- Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.

## How to Use

1. Read prompts/pairwise-testing.md and provide the objective, scope, material, environment, and evidence.
2. Complete known, missing, conflicting, stale, out_of_scope, and assumptions before findings.
3. Record PWT-## with factors, value pair, validity constraint, interaction risk, coverage rationale, source evidence, priority, and validation method, source, evidence state, impact, owner, close condition, and validation.
4. Preserve conflicts, unknown constraints, and open questions.

## Core Constraints

- do not describe pairwise coverage as all-combination coverage, ignore exclusions, or invent factors or values from experience.
- File presence, names, design declarations, and Eval configuration are not runtime evidence.
- Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention.
- Do not edit requirements, code, test assets, or target systems.

## Pre-delivery Check

- [ ] Recorded the six-part input audit.
- [ ] Every PWT-## has source, minimum evidence, impact/priority, owner role, close condition, and validation.
- [ ] Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
- [ ] Findings are not full cases, execution results, coverage proof, or release claims.

## Reference Files

- Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
- Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.

## Common Pitfalls

- Do not turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete.
- Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items.
- Do not expand this specialist design or review into a complete strategy, full test cases, runtime execution, or a release decision.

## Best Practices

- Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
- Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
- Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.