api-negative-testing

Use this skill when you need to design evidence-bounded API failure and rejection scenarios; triggers include API 负向测试 and API negative testing.

SKILL.md

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API Negative Testing

design invalid-input, rejection, and degradation candidates from contract, constraint, and error evidence. Produce ANT-## 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 negative testing candidates from API contracts, authentication and authorization constraints, parameter rules, error codes, rate-limit rules, defects, and raw responses.
  • 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

  1. Read prompts/api-negative-testing.md and provide the objective, scope, material, environment, and evidence.
  2. Complete the known, missing, conflicting, stale, out_of_scope, and assumptions input audit before findings.
  3. Record ANT-## with the subject, preconditions, behavior of concern, source evidence, and validation, plus impact/priority, owner role, close condition, and evidence state.
  4. Preserve conflicts, unknown constraints, and open questions when evidence is incomplete.

Domain Execution Essentials

  • Extract field types, required/null rules, ranges, length encoding, payload limits, identity/permission rules, and error contracts with sources.
  • Deliver constraint, input variant, allow/reject expectation, response/side-effect observation, and validation, tracing field and permission rules separately.
  • When deriving domain candidates, read and follow prompts/api-negative-testing.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 ANT-## 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.