Nw Sa Critique Dimensions Agent Skill
Architecture quality critique dimensions for peer review. Load when invoking solution-architect-reviewer or performing self-review of architecture documents.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/nWave-ai/nWave --skill nw-sa-critique-dimensionsThe instruction itself
19 sections, as written by the authorArchitecture Quality Critique Dimensions
Dimension 1: Architectural Bias Detection
Technology Preference Bias
Pattern: tech chosen by preference, not requirements. Detection: ADR lacks comparison matrix, choice not mapped to requirements, justified only as "best practice." Severity: HIGH.
Resume-Driven Development
Pattern: complex/trendy tech without requirement justification. Examples: microservices for 3-person team, Kafka for 100 req/day, service mesh without complexity. Detection: complexity exceeds team size/requirements, tech adds resume value not solves problem. Severity: CRITICAL.
Latest Technology Bias
Pattern: unproven tech (<6 months, small community) for production. Detection: check maturity, community, LTS, fallback plan. Severity: HIGH.
Dimension 2: ADR Quality Validation
Missing Context
ADR lacks business problem, technical constraints, or quality attribute requirements. Future maintainers cannot validate. Severity: HIGH.
Missing Alternatives Analysis
No alternatives (min 2 required). Each must be evaluated against requirements with rejection rationale. Severity: HIGH.
Missing Consequences
Omits positive/negative consequences and trade-offs. Quality attribute impact not analyzed. Severity: MEDIUM.
Dimension 3: Completeness Validation
Missing Quality Attributes
Architecture doesn't address required attributes. Verify: performance (latency, throughput) | scalability | security (auth, data protection) | maintainability (modularity, testability) | reliability (fault tolerance, recovery) | observability (logging, monitoring, alerting). Severity: CRITICAL.
Missing Performance Architecture
Performance requirements exist but no optimization strategy (caching, indexing, rate limiting, CDN). Severity: CRITICAL.
Dimension 4: Implementation Feasibility
Team Capability Mismatch
Requires expertise team lacks. Verify learning curve reasonable, training plan exists. Severity: HIGH.
Budget Constraints
Infrastructure costs exceed budget. Verify cost estimate exists and aligns. Severity: HIGH.
Testability Validation
Architecture prevents effective testing. Components must enable isolated testing with ports/adapters. Severity: CRITICAL.
Dimension 5: Priority Validation
Validate roadmap addresses largest bottleneck.
Q1: Largest bottleneck? (timing data must confirm primary problem)
Q2: Simpler alternatives considered? (rejected alternatives required)
Q3: Constraint prioritization correct? (quantified by impact, constraint-free first)
Q4: Data-justified? (key decision with quantitative data)
Failure: Q1=NO (wrong problem) | Q2=MISSING (no alternatives) | Q3=INVERTED (>50% solution for <30% problem) | Q4=NO_DATA for performance
Review Output Format
review_id: "arch_rev_{timestamp}"
reviewer: "solution-architect-reviewer"
artifact: "docs/product/architecture/brief.md, docs/product/architecture/adr-*.md"
iteration: {1 or 2}
strengths:
- "{Positive decision with ADR reference}"
issues_identified:
architectural_bias:
- issue: "{pattern detected}"
severity: "critical|high|medium|low"
location: "{ADR or section}"
recommendation: "{actionable fix}"
decision_quality:
- issue: "{ADR quality issue}"
severity: "high"
location: "ADR-{number}"
recommendation: "{add missing section}"
completeness_gaps:
- issue: "{quality attribute not addressed}"
severity: "critical"
recommendation: "{add architecture section}"
implementation_feasibility:
- issue: "{capability, budget, testability concern}"
severity: "high"
recommendation: "{simplify or add mitigation}"
priority_validation:
q1_largest_bottleneck:
evidence: "{data or NOT PROVIDED}"
assessment: "YES|NO|UNCLEAR"
q2_simple_alternatives:
assessment: "ADEQUATE|INADEQUATE|MISSING"
q3_constraint_prioritization:
assessment: "CORRECT|INVERTED|NOT_ANALYZED"
q4_data_justified:
assessment: "JUSTIFIED|UNJUSTIFIED|NO_DATA"
approval_status: "approved|rejected_pending_revisions|conditionally_approved"
critical_issues_count: {number}
high_issues_count: {number}
Severity Classification
- Critical: resume-driven dev, missing critical quality attributes, untestable, wrong problem
- High: technology bias, incomplete ADRs, feasibility concerns, missing data
- Medium: missing consequences, minor completeness gaps
- Low: documentation improvements, naming consistency
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How to use it
Take nwave-ai/nw-sa-critique-dimensions from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.