nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Critique dimensions and scoring for research document reviews
$ npx -y skills add nWave-ai/nWave --skill nw-rr-critique-dimensions --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nw-rr-critique-dimensionsContext preview
The summary Claude sees to decide when to auto-load this skill.
Critique dimensions and scoring for research document reviews
name: nw-rr-critique-dimensions description: Critique dimensions and scoring for research document reviews user-invocable: false disable-model-invocation: true
Load when reviewing research documents. Apply each dimension systematically.
Check: contradictory viewpoints included? | Multiple organizations/authors/perspectives? | Geographic/temporal diversity? | Sources truly independent (not circular)?
Flags: 60%+ from single org/author -> critical | All supporting same conclusion without counterpoint -> critical | Single geographic region -> medium | Clustered publication dates -> medium
Check: every major claim cited | sources reputable (peer-reviewed, official, established) | primary over secondary | technical sources recent (5 years) | confidence matches evidence
Flags: uncited claim -> high | blog/forum for factual claim -> high | all secondary sources -> medium | sources >5 years for tech -> medium | high confidence with 1-2 sources -> high
Check: search strategy documented | source selection criteria explicit | methodology transparent | confidence levels with rationale
Flags: no methodology section -> high | vague methodology ("searched the web") -> medium | no confidence ratings -> medium
For research driving architectural/strategic decisions.
Q1: Is this the largest bottleneck? (timing/measurement data?) | Q2: Simpler alternatives considered and rejected with evidence? | Q3: Constraint prioritization correct? (>50% solution for <30% problem = flag) | Q4: Key decision data-justified?
Flags: secondary concern addressed while larger exists -> critical | no measurement data for performance -> high | alternatives not documented -> high | prioritization not explicit -> medium
Output template:
priority_validation:
q1_largest_bottleneck:
evidence: "{timing data or 'NOT PROVIDED'}"
assessment: "YES|NO|UNCLEAR"
q2_simple_alternatives:
assessment: "ADEQUATE|INADEQUATE|MISSING"
q3_constraint_prioritization:
minority_constraint_dominating: "YES|NO"
assessment: "CORRECT|INVERTED|NOT_ANALYZED"
q4_data_justified:
assessment: "JUSTIFIED|UNJUSTIFIED|NO_DATA"
verdict: "PASS|FAIL"Check: knowledge gaps documented (what searched, why insufficient) | conflicting info acknowledged with credibility analysis | all required sections present (summary, findings, sources, gaps, citations) | research metadata included
Flags: missing gaps section when gaps exist -> critical | conflicting sources unacknowledged -> high | missing required sections -> high | no metadata -> medium
review_id: "research_rev_{timestamp}"
reviewer: "nw-researcher-reviewer (Scholar)"
issues_identified:
source_bias:
- issue: "{specific description with numbers}"
severity: "critical|high|medium"
recommendation: "{actionable fix}"
evidence_quality:
- issue: "{specific claim or location}"
severity: "critical|high|medium"
recommendation: "{actionable fix}"
replicability:
- issue: "{what is missing}"
severity: "critical|high|medium"
recommendation: "{actionable fix}"
priority_validation:
- issue: "{mismatch description}"
severity: "critical|high|medium"
recommendation: "{actionable fix}"
completeness:
- issue: "{missing element}"
severity: "critical|high|medium"
recommendation: "{actionable fix}"
quality_scores:
source_bias: 0.00
evidence_quality: 0.00
replicability: 0.00
completeness: 0.00
priority_validation: 0.00
approval_status: "approved|rejected_pending_revisions"
blocking_issues:
- "{critical issue 1}"
iteration: 1
max_iterations: 2AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton…
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating…