Equivalence Partitioning

Equivalence Partitioning Test Design

partition inputs into evidence-backed valid, invalid, and unknown classes based on constraints, rules, and response differences. Produce EP-## 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 Equivalence Partitioning Test Design candidates from input constraints, field types, business rules, role/state differences, error contracts, and existing cases.
• 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/equivalence-partitioning.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 EP-## with equivalence class, partition rationale, representative value, valid/invalid state, source evidence, expected concern, and validation method, source, evidence state, impact, owner, close condition, and validation.
4. Preserve conflicts, unknown constraints, and open questions.

Domain Execution Essentials

• Partition by sourced equivalent behavior/errors and constraints, not input shape alone; invalid classes with different responses must remain distinct.
• Deliver joint partitions, behavioral oracles and representatives, with disjointness/completeness scope and unknown classes; retain conditional validity.
• When deriving domain candidates, read and follow prompts/equivalence-partitioning.md for the domain procedure and oracle; retain valid controls, evidence gaps, and validation actions for each item.

Core Constraints

• do not merge classes from similar field names, invent error codes or rules, or treat one representative per class as full coverage.
• 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 EP-## 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.