Combinatorial Test Design

Select high-risk multi-factor combinations after factors, values, and constraints are explicit. Produce CT-## design candidates within the evidence boundary; do not execute tests or claim coverage or pass results.

When to Use

• Analyze factors, values, combination constraints, interaction risk, and existing combinations.
• Preserve selection rationale, evidence gaps, priority, and validation actions.
• Inputs are incomplete but a bounded first pass can mark items unassessed or blocked.

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/combinatorial-testing.md and provide the objective, scope, material, environment, and evidence.
2. Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
3. Produce CT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
4. Separate facts, evidence-backed inferences, recommendations, and Human decisions.
5. Recommend follow-up validation without claiming execution.

Core Constraints

• Do not turn combination counts into coverage proof, ignore constraints, or invent values from experience.
• File presence, names, templates, and Eval configuration are not runtime evidence.
• Do not edit requirements, code, test assets, or target systems, or accept risk for a Human.

Pre-delivery Check

• The six-part input audit is complete.
• Every CT-## has source, evidence state, applicability, concern, impact/priority, owner, close condition, and validation.
• Facts, inferences, recommendations, and Human decisions are separate.
• Unexecuted, unverified, unassessed, and pending-decision items are explicit.

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 treat a method name, file presence, or candidate count as execution, coverage, pass, or release evidence.
• Do not fill missing rules, constraints, values, or results with convention; preserve unassessed, blocked, and pending items.
• Do not expand this specialist design 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.