/nw-po-review-dimensions

name: nw-po-review-dimensions
description: Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation
user-invocable: false
disable-model-invocation: true

Requirements Quality Critique Dimensions

When invoked in review mode, apply these critique dimensions to requirements documents.

Persona shift: from requirements analyst to independent requirements reviewer. Focus: detect confirmation bias | validate completeness | ensure clarity and testability. Mindset: fresh perspective -- assume nothing, challenge assumptions, verify stakeholder needs.

Return complete YAML feedback to calling agent for display to user.

---

Dimension 0: Elevator Pitch Test (BLOCKING, checked first)

Every user story MUST contain an `### Elevator Pitch` subsection with three lines: Before / After / Decision enabled. The reviewer checks these invariants:

1. **Presence**: the section exists with all three lines. Missing → **BLOCKING**. 2. **Real entry point**: the "After" line references a user-invocable entry point (CLI subcommand, HTTP endpoint path, UI action) — not a service function, internal API, or test runner command. Internal-only → **BLOCKING**. 3. **Concrete output**: the "sees" clause describes observable output (stdout text sample, HTTP response body shape, rendered screen element) — not internal state, "tests pass", or "data is persisted". Internal state → **BLOCKING**. 4. **Job connection**: the "Decision enabled" line names a real decision the user makes with the output. If the story enables no user decision, it is infrastructure — BLOCK with recommendation to merge into a value-producing story. 5. **Slice-level check**: if every story in a slice is `@infrastructure`, the slice has no release value. **BLOCKING** at slice level — recommend re-slicing so that each slice contains at least one user-visible story.

Return this dimension first in the YAML feedback. If any BLOCKING issue is found, the overall review verdict MUST be `BLOCKED` regardless of other dimensions.

---

Dimension 1: Confirmation Bias Detection

Technology Bias

Pattern: requirements assume specific technology without stakeholder requirement. Examples: "Deploy to AWS" when deployment not discussed | "Use PostgreSQL" in requirements instead of architecture. Detection: check for technology specifics (cloud, database, frameworks). Verify stakeholder interviews mentioned these. Severity: HIGH (constrains solution space unnecessarily).

Happy Path Bias

Pattern: requirements focus on successful scenarios, minimal error/exception coverage. Examples: login documented but account lockout missing | payment success but fraud/timeout/decline not specified. Detection: count happy path stories vs error scenarios. Check each story has "sad path" alternatives. Severity: CRITICAL (incomplete requirements, production error handling missing).

Availability Bias

Pattern: requirements reflect recent experiences or familiar patterns over comprehensive analysis. Examples: "Same auth as previous project" without validating fit | requirements mirror competitor without stakeholder validation. Detection: check if requirements justified by stakeholder needs or "like previous project." Severity: MEDIUM (sub-optimal solution, missed opportunities).

---

Dimension 2: Completeness Validation

Missing Stakeholder Perspectives

Stakeholder groups to verify: end users (primary, secondary, occasional) | business owners/sponsors | operations/support teams | compliance/legal | technical teams. Detection: list stakeholder groups in requirements, check each group's needs represented, verify conflicting needs documented. Severity: HIGH.

Missing Error Scenarios

Required: invalid input validation | authentication/authorization failures | network timeouts | external service unavailability | data integrity violations | concurrent modification conflicts | resource exhaustion. Detection: for each user story, check for corresponding error scenarios. Severity: CRITICAL.

Missing Non-Functional Requirements

NFRs to validate: performance (latency, throughput) | security (auth, data protection) | scalability (concurrent users, data volume) | reliability (uptime, error rates) | compliance (regulatory, legal) | accessibility (WCAG). Detection: check NFR section exists, each NFR has measurable criteria, stakeholders provided expectations. Severity: CRITICAL.

---

Dimension 3: Clarity and Measurability

Vague Performance Requirements

Pattern: qualitative terms without quantitative thresholds. Vague: "System should be fast" | "User-friendly interface" | "Handle large volumes" | "Highly available." Detection: identify qualitative adjectives (fast, large, friendly, high, secure). Check for corresponding quantitative threshold. Severity: HIGH.

Ambiguous Requirements

Pattern: requirements interpretable multiple ways. Detection: check if two architects could design differently from same requirements. Look for multi-meaning words. Verify pronouns have clear antecedents. Severity: HIGH.

---

Dimension 4: Testability

Non-Testable Acceptance Criteria

Pattern: AC not observable, measurable, or automatable. Bad: "System should be easy to use" | "Code should be maintainable." Good: "User completes checkout in 3 or fewer clicks, 95% success rate" | "Cyclomatic complexity at most 10, test coverage at least 80%." Detection: for each AC, ask "Can an automated test verify this?" Check if AC specifies observable behavior with measurable pass/fail. Severity: CRITICAL.

---

Dimension 5: Priority Validation

Questions to Ask

**Q1: Is this the largest bottleneck?** Does timing data show this is the primary problem? Is there a larger problem being ignored?

**Q2: Were simpler alternatives considered?** Does the document include rejected alternatives? Are rejection reasons evidence-based?

**Q3: Is constraint prioritiza