9k tokens
context cost
the whole folder, loaded on every use
30
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
144
stars on the repo
on the repository, not the skill itself
Install
one command, takes just this skill from the repository
npx skills add https://github.com/naodeng/awesome-qa-skills --skill api-test-supertestCopy
What comes with it
32 015 bytes besides the instruction
README.md
agents/openai.yaml
evals/cases/basic-success.yaml
evals/cases/edge-bad-or-neighbor.yaml
evals/cases/edge-incomplete-input.yaml
evals/eval.yaml
examples/bruno/sample.bru
examples/ci/Jenkinsfile.supertest
examples/ci/github-actions-supertest.yml
examples/sample.curl
examples/sample.insomnia.json
examples/sample.openapi.yaml
examples/sample.opencollection.json
examples/sample.postman_collection.json
examples/sample.wsdl
output-templates/template-markdown.md
prompts/api-test-supertest.md
references/framework-spec.md
references/local/api-testing_EN.md
references/report-schema.md
references/setup-and-ci.md
scripts/generate_supertest_tests.py
scripts/parse_api_sources.py
scripts/run.sh
scripts/templates/supertest/app.js
scripts/templates/supertest/jest.config.cjs
scripts/templates/supertest/package.json
scripts/templates/supertest/tests/generated.api.test.js
scripts/templates/supertest/tests/health.test.js
The instruction itself
7 sections, as written by the author
api-test-supertest (EN)
中文版： See the corresponding Chinese skill.
When to Use
Need API outputs that should land in Supertest based automation.
The project is Node.js-based or already uses Supertest/Jest.
Workflow
Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
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-supertest.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: suite structure, environment setup, auth handling, priority endpoints, positive scenarios, negative and boundary scenarios, assertion focus, 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.