nw-dor-validation
Definition of Ready checklist criteria, antipattern detection patterns, UAT quality rules, and domain language enforcement for product owner review
Best use case
nw-dor-validation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Definition of Ready checklist criteria, antipattern detection patterns, UAT quality rules, and domain language enforcement for product owner review
Teams using nw-dor-validation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nw-dor-validation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nw-dor-validation Compares
| Feature / Agent | nw-dor-validation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Definition of Ready checklist criteria, antipattern detection patterns, UAT quality rules, and domain language enforcement for product owner review
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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"
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