api-test-pytest

name	api-test-pytest
description	Use this skill when you need to parse multi-format API definitions and generate Pytest API automation; triggers include Pytest API tests and API automation with Pytest.

api-test-pytest (EN)

中文版： See the corresponding Chinese skill.

When to Use

• Need API outputs that should land in pytest-based automation.
• The project is already Python-first or wants pytest-style structure.

Workflow

1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
2. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
3. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
4. Default to Markdown; switch formats only when the user asks.

Core Constraints

• Prioritize by risk / business impact — do not treat everything equally.
• Separate confirmed facts from current assumptions.
• Do not invent endpoints, fields, environments, or root causes the user did not provide.
• Use placeholders or env-var semantics for auth/secrets; never hardcode real credentials.
• Keep output executable: concrete scenarios, clear priority, clear next steps.

Progressive Disclosure

• Before producing output, read and follow prompts/api-test-pytest.md (minimum coverage, output structure, quality bar).
• When a ready-made template fits: use matching files under output-templates/.
• When the user wants examples or alignment with existing assets: read relevant examples/.
• For deep framework/troubleshoot/schema notes: read only the relevant file(s) under references/, do not load the whole directory.
• For format conversion or helper checks: prefer existing scripts/ over reinventing.
• For evaluating/regressing this skill: use evals/ with skill-up.

Pre-delivery Checklist

• Followed the main prompt's output structure
• Minimum coverage focus: module structure, fixture strategy, auth handling, priority endpoints, positive scenarios, negative and boundary scenarios, assertion focus, test data strategy, ... (details in main prompt)
• Covered the minimum checklist, or explained omissions
• High-risk items have explicit priority
• Did not invent details the user did not provide
• Assumptions and gaps are marked

Common Pitfalls

• Do not pretend completeness when scope/context is missing.
• Do not treat every item as equally important.
• Do not skip assumptions and information gaps.
• Do not dump generic theory unrelated to the current toolchain.