nw-ad-critique-dimensions
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
Best use case
nw-ad-critique-dimensions is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
Teams using nw-ad-critique-dimensions 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-ad-critique-dimensions/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nw-ad-critique-dimensions Compares
| Feature / Agent | nw-ad-critique-dimensions | 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?
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
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.
Related Guides
SKILL.md Source
# Acceptance Test Critique Dimensions
Load when performing peer review of acceptance tests (during *handoff-develop).
## Dimension 1: Happy Path Bias
**Pattern**: Only successful scenarios, error paths missing.
Detection: Count success vs error scenarios. Error should be at least 40%. Missing coverage examples: login success but no invalid password | Payment processed but no decline/timeout | Search results but no empty/error cases.
Severity: blocker (production error handling untested).
## Dimension 2: GWT Format Compliance
**Pattern**: Scenarios violate Given-When-Then structure.
Violations: Missing Given context | Multiple When actions (split into separate scenarios) | Then with technical assertions instead of business outcomes. Each scenario: Given (context), When (single action), Then (observable outcome).
Severity: high (tests not behavior-driven).
## Dimension 3: Business Language Purity
**Pattern**: Technical terms leak into acceptance tests.
Flag: database, API, HTTP, REST, JSON, classes, methods, services, controllers, status codes (500, 404), infrastructure (Redis, Kafka, Lambda).
Business alternatives: "Customer data is stored" not "Database persists record" | "Order is confirmed" not "API returns 200 OK" | "Payment fails" not "Gateway throws exception"
Severity: high (tests coupled to implementation).
## Dimension 4: Coverage Completeness
**Pattern**: User stories lack acceptance test coverage.
Validation: Map each story to scenarios | Verify all AC have corresponding tests | Confirm edge cases and boundaries tested.
Severity: blocker (unverified requirements).
## Dimension 5: Walking Skeleton User-Centricity
**Pattern**: Walking skeletons describe technical layer connectivity instead of user value.
Detection litmus test for `@walking_skeleton` scenarios:
- Title describes user goal or technical flow?
- Then steps describe user observations or internal side effects?
- Could non-technical stakeholder confirm "yes, that is what users need"?
Violations: "End-to-end order flow through all layers" (technical framing) | Then "order row inserted in database" (internal side effects) | Given "database contains user record" instead of "customer has an account"
Severity: high (skeletons that only prove wiring miss the point -- first skeleton should be demo-able to stakeholder).
## Dimension 6: Priority Validation
**Pattern**: Tests address secondary concerns while larger gaps exist.
Questions: 1. Is this the largest bottleneck? (timing data or gap analysis) | 2. Simpler alternatives considered? | 3. Constraint prioritization correct? | 4. Test design decisions data-justified?
Severity: blocker if wrong problem addressed, high if no measurement data.
## Dimension 7: Observable Behavior Assertions
**Pattern**: Tests assert internal state or method calls instead of observable behavior.
For EVERY Then step in EVERY scenario, apply this mechanical checklist:
1. Does the assertion check a return value from a driving port call? YES = pass, NO = flag.
2. Does the assertion check an observable outcome (user sees X, system produces Y)? YES = pass, NO = flag.
3. Does the assertion check internal state, private fields, or method call counts? YES = REJECT the scenario.
**Concrete violations to flag**:
- `assert mock_repo.save.called` — asserts method call, not observable outcome
- `assert len(db.query(Order).all()) == 1` — asserts internal DB state
- `assert obj._internal_field == "value"` — asserts private state
- `assert os.path.exists("output.json")` — asserts file existence (implementation detail)
**Concrete passing assertions**:
- `assert result.is_confirmed()` — observable business outcome
- `assert result.order_number is not None` — return value from driving port
- `assert "confirmation" in customer_notification.subject` — observable user outcome
**Relationship to Dim 5 (Walking Skeleton User-Centricity)**:
- Dim 5 validates walking skeleton SCOPE (user goal framing vs technical layer framing)
- Dim 7 validates ASSERTION TYPE for ALL scenarios (walking skeletons AND focused scenarios)
- A scenario can pass Dim 5 (good user-centric framing) and fail Dim 7 (internal state assertions)
Severity: high (tests coupled to implementation break on refactoring).
## Dimension 8: Traceability Coverage
**Pattern**: Scenarios exist without traceability to upstream wave artifacts.
Two mandatory traceability checks:
**Check A — Story-to-Scenario mapping**:
1. Read `docs/feature/{feature-id}/discuss/user-stories.md`
2. Extract ALL story IDs (e.g., US-01, US-02)
3. For EACH story ID, verify at least one scenario references it (via tag or comment)
4. Flag EVERY story ID with zero matching scenarios as BLOCKER
**Check B — Environment-to-Scenario mapping**:
1. Read `docs/feature/{feature-id}/devops/environments.yaml`
2. If missing, use defaults: `clean`, `with-pre-commit`, `with-stale-config`
3. For EACH environment, verify at least one walking skeleton includes a Given clause referencing that environment's preconditions
4. Flag EVERY environment with zero matching Given clauses as HIGH
**What this dimension does NOT cover**:
- KPI measurability — that is PO-reviewer scope during DELIVER post-merge gate
- Scenario quality — covered by Dims 1-7
Severity: blocker for Check A (untraceable requirements), high for Check B (untested environments).
## Review Output Format
```yaml
review_id: "accept_rev_{timestamp}"
reviewer: "acceptance-designer (review mode)"
strengths:
- "{positive test design aspect with example}"
issues_identified:
happy_path_bias:
- issue: "Feature {name} only tests success"
severity: "blocker"
recommendation: "Add error scenarios: invalid input, timeout, service failure"
gwt_format:
- issue: "Scenario has multiple When actions"
severity: "high"
recommendation: "Split into separate scenarios"
business_language:
- issue: "Technical term '{term}' in scenario"
severity: "high"
recommendation: "Replace with: '{business alternative}'"
coverage_gaps:
- issue: "User story {US-ID} has no acceptance tests"
severity: "blocker"
recommendation: "Create scenarios for all AC of {US-ID}"
walking_skeleton_centricity:
- issue: "Walking skeleton '{name}' describes technical flow, not user goal"
severity: "high"
recommendation: "Reframe: title as user goal, Then steps as observable user outcomes"
observable_behavior:
- issue: "Scenario '{name}' Then step asserts internal state: {assertion}"
severity: "high"
recommendation: "Replace with observable outcome assertion: {alternative}"
traceability_coverage:
- issue: "Story {US-ID} has no matching scenario"
severity: "blocker"
recommendation: "Create at least one scenario tagged @{US-ID}"
- issue: "Environment '{env}' has no matching Given clause in walking skeletons"
severity: "high"
recommendation: "Add walking skeleton with Given clause: 'Given a {env} environment with {preconditions}'"
walking_skeleton_boundary:
- issue: "WS strategy not declared in wave-decisions.md"
severity: "blocker"
recommendation: "Auto-detect strategy and confirm with user"
- issue: "WS uses @in-memory under Strategy {C/B/D} for local resource adapter"
severity: "blocker"
recommendation: "Replace InMemory with real adapter (tmp_path, real subprocess)"
- issue: "Driven adapter '{name}' has no real I/O integration test"
severity: "blocker"
recommendation: "Add integration test with real I/O for this adapter"
approval_status: "approved|rejected_pending_revisions|conditionally_approved"
```
## Reviewer Scope Boundaries
The acceptance-designer-reviewer (Sentinel) owns Dimensions 1-9 during DISTILL.
Responsibilities that belong to OTHER reviewers (do NOT evaluate these):
- **KPI measurability**: PO-reviewer validates during DELIVER post-merge gate
- **Infrastructure readiness**: PA-reviewer validates during DEVOPS-to-DISTILL handoff
- **Code quality**: Software-crafter-reviewer validates during DELIVER Phase 4
If a finding touches KPI measurement or infrastructure readiness, tag it `@escalate:{reviewer}` in the review output and move on. Do NOT attempt to evaluate it.
## Dimension 9: Walking Skeleton Boundary Proof
For walking skeleton scenarios, validate that the WS actually proves adapter wiring with real I/O.
### 9a: WS Strategy Declaration
Is the WS strategy declared in wave-decisions.md?
- NOT declared: BLOCKER (ask the user to confirm auto-detected strategy)
### 9b: WS Strategy-Implementation Match
Does the WS implementation match the declared strategy?
- Strategy C declared but WS uses @in-memory for all adapters: BLOCKER
- Strategy B declared but no @requires_external marker for costly deps: HIGH
### 9c: Adapter Integration Coverage
Does every driven adapter have a real I/O integration test?
- Missing adapter test: BLOCKER regardless of WS strategy
### 9d: Walking Skeleton Fixture Tier
Walking skeleton fixtures — what adapter tier do they use?
- Litmus test: "If I deleted the real adapter, would this WS still pass?"
- If YES for a local resource adapter: WS is testing InMemory, not wiring. REJECT.
### 9e: Strategy Drift Detection
Grep for @in-memory on walking skeleton scenarios under strategies B/C/D.
- If found: HIGH — WS claims real adapters but uses InMemoryRelated Skills
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