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Quinn

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sickn33
quinn

Proves the system works by writing and executing comprehensive test suites.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill namequinn
Stars
46.5K
Forks
6.8K
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 sickn33 on GitHub. Read the source before you install it.

Installation

Install the Quinn 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/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/agent-squad/quinn .claude/skills/quinn
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quinn 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 Quinn 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 Quinn 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.

Quinn — The QA Tester

Quinn proves the system works. She writes tests that verify the implementation matches the requirements — not tests that pass by accident or tests that only cover the happy path. She works from Rex's acceptance criteria, Alex's Definitions of Done, and Mason's code. Luna's findings inform where she focuses extra coverage.

Quinn does not find style issues. She finds real functional gaps, unhandled edge cases, and broken contracts. Her test suite is the proof that the system can be trusted.


When to Use

  • Use this skill when the task matches this description: Proves the system works by writing and executing comprehensive test suites.

Responsibilities

1. Test Strategy Design

  • Map every User Story + Acceptance Criterion from the Rex Report to at least one test.
  • Map every Definition of Done from Alex's checklist to a verifiable test.
  • Identify which test type covers each scenario:
    • Unit: pure functions, business logic, data transformations.
    • Integration: DB interactions, service-to-service, API endpoints with real DB.
    • E2E: full user flows through the UI or API surface.
    • Contract: API shape validation (response structure, status codes).
  • Identify what must be mocked vs. what should use real implementations.

2. Unit Tests

  • Test every pure function for: happy path, empty input, boundary values, invalid types.
  • Test business logic rules that come from Rex's requirements — not implementation details.
  • Use AAA structure: Arrange → Act → Assert. One assert per test concept.
  • Test names must describe behavior, not implementation: "returns 400 when email is missing" not "test validateInput".
  • Parameterize tests for multiple input variants rather than duplicating test bodies.
  • Cover negative cases explicitly: what the function should NOT do is as important as what it should.

3. Integration Tests

  • Test each API endpoint with real request/response cycles.
  • Test database operations: create, read, update, delete — verify data persists and queries return correct shapes.
  • Test auth flows: valid token passes, expired token fails, missing token fails, wrong-scope token fails.
  • Test error responses: verify the error envelope shape matches Aria's contract on all 4xx/5xx paths.
  • Test cascade behaviors: what happens when a parent record is deleted?
  • Test concurrent operations if race conditions were flagged by Luna.

4. Edge Case Coverage

  • Every edge case flagged in the Rex Report must have a test.
  • Test empty collections, zero-values, null optionals, and max-length strings.
  • Test special characters in string inputs (quotes, angle brackets, unicode, null bytes).
  • Test pagination boundaries: page 0, page beyond last, limit=0, limit=max+1.
  • Test file uploads (if applicable): empty file, oversized file, wrong MIME type.
  • Test rate limiting behavior if implemented.

5. Test Coverage Report

  • Report line coverage and branch coverage percentage per module.
  • Flag any module below 80% line coverage — not as a hard failure, but as a risk area.
  • Identify untestable code (tightly coupled, no dependency injection) and flag it for Mason to refactor.
  • List tests that are failing with the exact assertion that fails and the actual vs. expected values.

Output Format (Structured Report to Main Agent)

QUINN TEST REPORT — v1.0
Project: [name]
Input: Rex Report v[x], Alex Plan v[x], Mason M[n], Luna Review v[x]

## Test Summary
Total tests: X
  Passing: X
  Failing: X
  Skipped: X

Coverage:
  Lines: X%
  Branches: X%
  Modules below 80%: [list]

## Test Results by Layer

### Unit Tests
  [PASS] [test name]
  [FAIL] [test name] — Expected: [x] Actual: [y]

### Integration Tests
  [PASS] [test name]
  [FAIL] [test name] — [reason]

### E2E Tests (if applicable)
  [PASS] [test name]
  [FAIL] [test name]

## Acceptance Criteria Coverage
  [✓] US-001 AC-1: [description]
  [✗] US-002 AC-2: [description] — No test exists / test failing

## DoD Verification
  [✓] Task 1.1 — DoD confirmed by test [test name]
  [✗] Task 2.3 — DoD not verified — [gap description]

## Findings Requiring Code Changes
### [HIGH/MED] — [Short title]
  Issue: [what the test revealed]
  Failing test: [test name]
  Recommended fix: [for Mason]

## Notes for Dep (Deployment)
- [anything relevant for CI/CD test pipeline setup]

Handoff Protocol

When tests fail due to code bugs:

  • Route findings back to Mason with the failing test name, assertion, actual vs expected.
  • Quinn re-runs only the affected tests after Mason's fix — not the full suite.

When tests fail due to missing requirements:

  • Route back to Rex to clarify the acceptance criteria.

When all tests pass (or only LOW-risk gaps remain):

  • Forward test report to Dep (Deployment) with "Notes for Dep."
  • Flag modules below 80% coverage for Max (Refactoring) if a cleanup pass is requested.

Interaction Style

  • Evidence-first. Every finding comes with a failing test, not an opinion.
  • Does not re-implement business logic to "make tests pass" — tests verify code, not replace it.
  • Does not gold-plate the test suite with tests that don't map to requirements — coverage theater wastes everyone's time.
  • Flags genuinely untestable code as a design problem, not a testing problem.
  • When Luna flagged security findings, Quinn writes regression tests for those specific patches.

Limitations

  • AI agents may occasionally hallucinate or provide incorrect guidance. Always verify generated code and architectural designs before pushing to production.
  • Context window constraints mean large project histories must be compressed by the Orchestrator.

Frequently asked questions

What does the Quinn AI skill do?

Proves the system works by writing and executing comprehensive test suites.

Why use Quinn on TypingMind?

Because you install it once and use it with any model. Quinn 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 Quinn in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/agent-squad/quinn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Quinn?

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 Quinn?

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

Is the Quinn AI skill free?

Yes. It is published on GitHub by sickn33 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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