mcpField
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api-contract-testing

api-contract-testing

github.com/naodengTesting & quality28941 installsUpdated today
Repository

About api-contract-testing

A skill for reviewing and designing provider-consumer API contract tests across schemas, semantics, compatibility, and evolution. It emphasizes evidence-based, risk-ranked findings and actionable, verifiable criteria, including when input is incomplete.

Key features

  1. 01Schema and semantic coverage
  2. 02Compatibility and evolution review
  3. 03Evidence-linked findings
  4. 04Risk-ranked recommendations
  5. 05Machine-readable output checks

Use cases

  1. 01Reviewing an API contract test plan
  2. 02Assessing a proposed API change
  3. 03Improving contract test evidence
  4. 04Preparing structured review output

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/naodeng/awesome-qa-skills --skill api-contract-testing

After installing, type/api-contract-testingin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
evals/
eval.yaml
evals/cases/
basic-success.yaml
edge-incomplete-input.yaml
edge-risk-priority.yaml
review-compatible-additive-control.yaml
review-schema-versus-semantics.yaml
prompts/
api-contract-testing.md

Details

From the project README

API Contract Testing

When to Use

  • Use this skill when you need to verify provider-consumer API contracts across schemas, semantics, compatibility, and evolution.
  • Use it to review an existing plan, result, or evidence set and produce actionable improvements.
  • Use it when context is incomplete but a bounded first pass is still valuable.

Output Format Options

  • Default to Markdown for review, execution, and incremental refinement.
  • When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
  • For machine-consumed output, confirm the schema, enums, and required fields first.

How to Use

  1. Read and follow prompts/api-contract-testing.md, including its input contract, execution rules, minimum coverage, and output order.
  2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
  3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
  4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
  5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.

Reference Files

  • Always read prompts/api-contract-testing.md; it is the complete execution specification for this skill.
  • For evaluation or regression, read evals/eval.yaml and the relevant cases under evals/cases/.
  • Load references/, examples/, scripts/, or output-formats.md only when those directories exist and the task needs them.

Core Constraints

  • contracts include semantics and error behavior, not only schemas
  • do not infer full contracts from examples
  • breaking changes require a migration path
  • Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
  • Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
  • Explain priority using business impact, likelihood, or detectability.

Delivery Checklist

  • Covered: fields and types, required and nullable rules, enums, status codes, error bodies, compatibility, version evolution, consumer impact.
  • Separated facts, assumptions, gaps, and recommendations.
  • Gave high-risk items a priority, evidence basis, owner or next action.
  • Defined verifiable decision criteria instead of generic advice.
  • Performed no unauthorized production writes or destructive actions.

Common Pitfalls

  • Listing checks without preconditions, expected outcomes, or evidence.
  • Marking everything high priority and avoiding tradeoffs.
  • Substituting tool names or generic theory for domain reasoning.
  • Refusing incomplete input, or pretending incomplete evidence supports certainty.

Best Practices

  • Start with paths most likely to cause business loss, safety issues, or release blockage.
  • Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
  • Make the artifact executable and independently reviewable by another engineer.

Comments

Markdown supported