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Testing Principles

Community
shinpr
testing-principles

Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill nametesting-principles
Stars
682
Forks
103
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by shinpr on GitHub. Read the source before you install it.

Installation

Install the Testing Principles AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-skills/skills/testing-principles .claude/skills/testing-principles
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing Principles in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Testing Principles on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Testing Principles is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Language-Agnostic Testing Principles

Test-Driven Development (TDD)

Use this cycle for new or changed executable behavior and reproducible bug fixes. For a behavior-preserving refactor, first confirm existing tests pass or add passing characterization tests, then refactor and rerun the same regression evidence.

RED: confirm the new test fails for the intended reason. GREEN: implement the smallest passing change. REFACTOR: improve structure while the test remains green. VERIFY: run the repository's applicable regression checks.

Quality Requirements

  • Treat coverage as a diagnostic signal for finding untested areas, not a target — a target gets gamed into trivial tests (Goodhart's Law)
  • Concentrate tests on critical paths, business logic, and behavior whose regression would matter
  • Prioritize meaningful assertions over the coverage number; any CI threshold is the project's config, not a quality goal in itself
  • Use project-configured speed budgets when present. Otherwise investigate test speed only when observed feedback or CI cost is material to the current outcome; retain slower tests when their proof boundary requires it

Test Design Rules

  • Structure each test as Arrange, one Act, and Assert; multiple assertions may prove one behavior.
  • Follow the repository's test naming convention and name the condition and observable outcome.
  • Exercise behavior through a public or integration boundary. Assertions verify return values, outputs, errors, or state changes rather than private implementation.
  • Use independently derived literal, property, approved snapshot, or fixture expectations. An implementation-derived oracle cannot detect the same implementation defect.
  • Keep each test's expected outcome unconditional. Table-driven or property-based cases are acceptable when each case is reported distinctly and uses an independent oracle.
  • Cover accepted boundary and error behavior; derive cases from the contract instead of adding generic edge-case permutations.
  • Each test creates and cleans up its own state, passes in isolation and any order, and controls time or randomness that affects its result.
  • Keep tests executable. Fix or remove tests that no longer describe accepted behavior; restore tests disabled only to bypass a failure.

Mock and Boundary Rules

  • Mock direct external I/O boundaries; keep internal business logic and the boundary under test real.
  • Use the existing application-owned adapter as the mock boundary. Introduce an adapter only when external I/O, an unstable contract, or required substitution cannot be controlled through the current design.
  • Keep mock behavior limited to the contract needed by the test.

Data Layer Testing

Mock-based tests are sufficient when data access is only a dependency of the behavior under test. Verify against the project's real database engine or its accepted equivalent when the subject is a query, repository implementation, schema constraint, or migration compatibility. Resolve the test environment from repository configuration; when no representative environment exists and adding one is outside the approved work, report the missing verification decision.

Cross-check data-access code against the schema source named in the Design Doc or repository configuration. Schema-source verification is required to prove table, column, type, constraint, or dialect compatibility; successful mocks prove behavior only at the mocked boundary.

Verification Requirements

Capability Probe Postconditions

A capability probe passes when it uses the consumer's boundary and asserts the exact property that consumer needs. Command success, import success, or object existence is setup evidence.

Test Organization

Follow the repository's established test paths, runner routing, and naming. When establishing an approved new convention, separate test types only when their setup, runner, or environment differs.

Regression Testing

  • Add a regression test for every reproducible behavior bug fix. When executable reproduction is impossible, record the reason and the alternative static, contract, or environment evidence that prevents recurrence.
  • Before behavior-preserving changes to uncharacterized legacy code, establish passing characterization evidence and rerun it after the change.

Frequently asked questions

What does the Testing Principles AI skill do?

Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.

Why use Testing Principles on TypingMind?

Because you install it once and use it with any model. Testing Principles is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Testing Principles in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-skills/skills/testing-principles. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testing Principles?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Testing Principles?

As many as you like. As long as a model supports skills, you can use Testing Principles with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Testing Principles AI skill free?

Yes. It is published on GitHub by shinpr under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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