API Schema Validation
compare API schema constraints with sourced request, response, and version evidence. Produce ASV-## findings. This Skill organizes traceable API-quality candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.
When to Use
- When you need API schema validation candidates from OpenAPI, JSON Schema, GraphQL schemas, endpoint inventories, request-response samples, versions, and validation reports.
- 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 contract or behavior, 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
- Read prompts/api-schema-validation.md and provide the objective, scope, material, environment, and evidence.
- Complete the known, missing, conflicting, stale, out_of_scope, and assumptions input audit before findings.
- Record ASV-## with the subject, preconditions, behavior of concern, source evidence, and validation, plus impact/priority, owner role, close condition, and evidence state.
- Preserve conflicts, unknown constraints, and open questions when evidence is incomplete.
Domain Execution Essentials
- Identify OpenAPI/JSON Schema/GraphQL and its version/dialect, operation/direction, content type, reference root and resolution context. Suspend version-dependent decisions when unknown.
- Deliver dialect, reference-resolution status, and sample/constraint decisions; required presence and a null value must be assessed separately.
- When deriving domain candidates, read and follow prompts/api-schema-validation.md for the domain procedure and oracle; retain valid controls, evidence gaps, and validation actions for each item.
Core Constraints
- Do not execute tests, assume missing rules, versions, thresholds, data, or responses, or treat candidate counts as coverage proof.
- 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 known, missing, conflicting, stale, out_of_scope, and assumptions input audit.
- Every ASV-## has source, evidence state, impact/priority, owner role, close condition, and validation.
- Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
- Findings are not 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.
