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 prioritization correct? Are user-mentioned constraints quantified by impact? Is a minority constraint dominating the solution?

Q4: Is the approach data-justified? Is the key decision supported by quantitative data? Would different data lead to different approach?

Failure Conditions

• FAIL if Q1 = NO (wrong problem addressed)
• FAIL if Q2 = MISSING (no alternatives considered)
• FAIL if Q3 = INVERTED (minority constraint dominating)
• FAIL if Q4 = NO_DATA and this is performance optimization

Review Output Format

yaml
review_id: "req_rev_{YYYYMMDD_HHMMSS}"
reviewer: "product-owner (review mode)"
artifact: "{document path}"
iteration: {1 or 2}

strengths:
  - "{Positive aspect with specific example}"

issues_identified:
  confirmation_bias:
    - issue: "{Specific bias detected}"
      severity: "critical|high|medium|low"
      location: "{Section or US-ID}"
      recommendation: "{How to address}"

  completeness_gaps:
    - issue: "{Missing stakeholder/scenario/NFR}"
      severity: "critical|high"
      location: "{Section}"
      recommendation: "{What to add}"

  clarity_issues:
    - issue: "{Vague or ambiguous requirement}"
      severity: "high"
      location: "{Requirement ID}"
      recommendation: "{How to clarify}"

  testability_concerns:
    - issue: "{Non-testable AC}"
      severity: "critical"
      location: "{AC-ID}"
      recommendation: "{How to make testable}"

  priority_validation:
    q1_largest_bottleneck: "YES|NO|UNCLEAR"
    q2_simple_alternatives: "ADEQUATE|INADEQUATE|MISSING"
    q3_constraint_prioritization: "CORRECT|INVERTED|NOT_ANALYZED"
    q4_data_justified: "JUSTIFIED|UNJUSTIFIED|NO_DATA"
    verdict: "PASS|FAIL"

approval_status: "approved|rejected_pending_revisions|conditionally_approved"
critical_issues_count: {number}
high_issues_count: {number}

Severity Classification

• Critical: non-testable AC | missing error scenarios | missing NFRs | wrong problem addressed
• High: technology bias | happy path bias | vague requirements | missing stakeholders
• Medium: availability bias | minor completeness gaps | ambiguous wording
• Low: documentation formatting | terminology consistency