nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
LeanUX backlog management methodology - user story template, story sizing, story states, task types, Definition of Ready/Done, anti-pattern detection and remediation
$ npx -y skills add nWave-ai/nWave --skill nw-leanux-methodology --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nw-leanux-methodologyContext preview
The summary Claude sees to decide when to auto-load this skill.
LeanUX backlog management methodology - user story template, story sizing, story states, task types, Definition of Ready/Done, anti-pattern detection and remediation
name: nw-leanux-methodology description: LeanUX backlog management methodology - user story template, story sizing, story states, task types, Definition of Ready/Done, anti-pattern detection and remediation user-invocable: false disable-model-invocation: true
"A backlog is not a todo list. It's a collection of validated hypotheses waiting to become working software."
| State | Meaning | Entry Criteria | |-------|---------|----------------| | Draft | Idea captured, not validated | Has problem statement | | Ready | Validated, has UAT, ready to build | All DoR items complete | | In Progress | Actively being built | UAT test written (RED) | | In Review | Code complete, awaiting review | All tests green | | Done | Merged, deployed, validated | UAT passes in production | | Blocked | Cannot proceed | Blocker documented |
Completable in 1-3 days | 3-7 UAT scenarios | Delivers demonstrable value | Explainable in 2 minutes
>7 UAT scenarios | >3 days effort | Multiple distinct user outcomes | Cannot demonstrate in single session
Split by user outcome, not technical layer. Each resulting story delivers independently demonstrable value.
Example: "User Management" (20 scenarios) splits into:
Stories pass ALL 8 items before proceeding to DESIGN wave.
**1. Problem statement clear and in domain language**
**2. User/persona identified with specific characteristics**
**3. At least 3 domain examples with real data**
**4. UAT scenarios in Given/When/Then (3-7 scenarios)**
**5. Acceptance criteria derived from UAT**
**6. Story right-sized (1-3 days, 3-7 scenarios)**
**7. Technical notes identify constraints**
**8. Dependencies resolved or tracked**
## Definition of Ready Validation
### Story: {story-id}
| DoR Item | Status | Evidence/Issue |
|----------|--------|----------------|
| Problem statement clear | PASS/FAIL | {evidence or issue} |
| User/persona identified | PASS/FAIL | {evidence or issue} |
| 3+ domain examples | PASS/FAIL | {evidence or issue} |
| UAT scenarios (3-7) | PASS/FAIL | {evidence or issue} |
| AC derived from UAT | PASS/FAIL | {evidence or issue} |
| Right-sized | PASS/FAIL | {evidence or issue} |
| Technical notes | PASS/FAIL | {evidence or issue} |
| Dependencies tracked | PASS/FAIL | {evidence or issue} |
### DoR Status: PASSED / BLOCKEDWhen DoR fails: 1. Display specific failures with remediation guidance 2. Do not proceed to peer review or handoff 3. Return to user with action items 4. Re-validate after fixes applied
DoD validation owned by acceptance-designer during DISTILL->DELIVER transition. Product-owner defines checklist, acceptance-designer enforces.
Checklist:
AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton…
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating…