nw-po-review-dimensions

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Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation

Code & Development 526 stars 55 forks Updated 1 weeks ago MIT

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Quality Score: 92/100

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91
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90
Frontmatter 20%
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50
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Description 5%
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Skill Content

# 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 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...

Details

Author
nWave-ai
Repository
nWave-ai/nWave
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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