Nw Dor Validation logo

Nw Dor Validation

Organization
nWave-ai
nw-dor-validation

Definition of Ready checklist criteria, antipattern detection patterns, UAT quality rules, and domain language enforcement for product owner review

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-dor-validation
Stars
616
Forks
65
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 nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Dor Validation 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.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-dor-validation
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-dor-validation .claude/skills/nw-dor-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Dor Validation 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 Nw Dor Validation 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 Nw Dor Validation 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.

DoR Validation and Antipattern Detection

Definition of Ready Checklist (8 Items - Hard Gate)

All items must PASS with evidence. Each FAIL blocks handoff.

Item 1: Problem Statement Clear and Validated

Domain language (not technical), describes real user pain, testable. Pass: "Maria wastes 30 seconds typing credentials on every visit" Fail: "Users need authentication" | "Implement login feature"

Item 2: User/Persona with Specific Characteristics

Real name, specific role, clear context. Pass: "Maria Santos, returning customer (2+ orders), using trusted MacBook" Fail: "User" | "Customer" | "End user" | "Authenticated user"

Item 3: At Least 3 Domain Examples with Real Data

Min 3 examples, real names (not user123), real values, different scenarios (happy/edge/error). Pass: "Example 1: Maria on MacBook, 5 days since login, goes to dashboard" Fail: "User logs in successfully" | "Test with valid credentials"

Item 4: UAT Scenarios Cover Happy Path + Edge Cases

Given/When/Then format, 3-7 scenarios, real data, covers happy + edge. Pass: "Given Maria authenticated on 'MacBook-Home' 5 days ago..." Fail: "Test login works" | "Given a user When they login Then success"

Item 5: Acceptance Criteria Derived from UAT

Checkable (checkbox), traceable to UAT, outcome-focused (not implementation). Pass: "Sessions older than 30 days require re-authentication" Fail: "Use JWT tokens" | "System should work correctly"

Item 6: Story Right-Sized (1-3 Days, 3-7 Scenarios)

Effort estimate provided, scenario count in range, single demonstrable outcome. Pass: 2 days, 5 UAT scenarios, demoed in single session Fail: >7 scenarios | >3 days | multiple distinct outcomes

Item 7: Technical Notes Identify Constraints

Dependencies listed, risks identified, architectural considerations noted. Pass: "Requires JWT token storage, GDPR cookie consent integration" Fail: no technical notes section

Item 8: Dependencies Resolved or Tracked

Blocking deps identified, resolution status clear, escalation path. Pass: "Depends on US-041 (completed) and Auth service API (available)" Fail: "Needs some API - TBD"


Antipattern Detection (8 Patterns)

1. Implement-X (critical)

Signal: starts with "Implement", "Add", "Create", "Build", "Develop" Detection: ^(Implement|Add|Create|Build|Develop)\s | Fix: rewrite as user pain

2. Generic Data (high)

Signal: user123, test@test.com, foo, bar, lorem, placeholder Detection: user[0-9]+, test@, example@, foo, bar | Fix: real names -- Maria Santos

3. Technical AC (high)

Signal: AC describes implementation not outcome Detection: "Use JWT", "Implement using", "Database should", "API must return" Fix: outcome focus -- "Session persists for 30 days"

4. Giant Stories (critical)

Signal: >7 scenarios | >3 days | multiple distinct outcomes | Fix: split by user outcome

5. No Examples (critical)

Signal: no "Example" section | <3 examples | abstract examples | Fix: add 3+ with real data

6. Tests After Code (high)

Signal: "Tests to be added", "Will write tests later", "Tests TBD" | Fix: UAT first, RED first

7. Vague Persona (high)

Signal: "User", "Customer", "End user" as persona | Fix: "Maria Santos, returning customer (2+ orders)"

8. Missing Edge Cases (medium)

Signal: all success scenarios, no errors, no boundaries | Fix: add expired session, invalid device, etc.


UAT Scenario Quality Checks

Format: Given/When/Then with complete sentences. Fail: "Test login", "Given user When login Then success" Real Data: real names, values, scenarios. Fail: "Given user123", "When X happens" Coverage: min 1 happy path + 1 edge + 1 error. Range: 3-7 scenarios.


Domain Language Checks

Technical Jargon: flag in user-facing sections: JWT, API, database, backend, frontend, microservice, REST, HTTP, JSON, SQL. Exception: Technical Notes section. Fix: "session token" -> "remember me"

Generic Language: flag "the system", "the application", "functionality", "feature". Fix: use specific names -- "the login page" -> "the welcome screen"

Frequently asked questions

What does the Nw Dor Validation AI skill do?

Definition of Ready checklist criteria, antipattern detection patterns, UAT quality rules, and domain language enforcement for product owner review

Why use Nw Dor Validation on TypingMind?

Because you install it once and use it with any model. Nw Dor Validation 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 Nw Dor Validation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-dor-validation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Dor Validation?

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 Nw Dor Validation?

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

Is the Nw Dor Validation AI skill free?

Yes. It is published on GitHub by nWave-ai 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇