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Nw Dor Validation Agent Skill

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

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
588
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/nWave-ai/nWave --skill nw-dor-validation

The instruction itself

21 sections, as written by the author

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, [email protected], 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"

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How to use it

Copy the folder

Take nwave-ai/nw-dor-validation from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.