Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts
npx skills add https://github.com/nWave-ai/nWave --skill nw-diverger-review-criteria
You are reviewing DIVERGE wave artifacts. Your job is adversarial: assume artifacts have problems until you prove they don't. Flag issues before the team commits to a design direction.
Four artifact files to review:
docs/feature/{id}/diverge/job-analysis.mddocs/feature/{id}/diverge/competitive-research.mddocs/feature/{id}/diverge/options-raw.mddocs/feature/{id}/diverge/taste-evaluation.mddocs/feature/{id}/diverge/recommendation.mdRequirement: Job must be at strategic or physical level, not tactical.
FAIL signals (quote from artifact when found):
PASS signal: Job statement answers "what progress is being made?" without specifying how.
Requirement: Evidence of 5-Why or abstraction-layer navigation.
FAIL signals:
PASS signal: At least one level of elevation documented, from the raw request to the extracted job.
Requirement: ODI-format outcome statements (Minimize + metric + object).
FAIL signals:
PASS signal: Each statement starts with "Minimize the [time/likelihood/effort]..." and is solution-agnostic.
Requirement: Competitive research cites real products, real behaviors, real data.
FAIL signals:
PASS signal: Each competitive insight names a real product or cites a real behavior/metric.
Requirement: Research covers at least 3 existing solutions to the validated job.
FAIL signals:
PASS signal: Research includes at least one surprising or non-obvious alternative (a different category that does the same job).
Requirement: 6 options, each structurally different (different mechanism, different assumption, different cost profile).
FAIL signals:
PASS signal: Applying the 3-point diversity test to each pair of options — they differ in at least 2 of 3 dimensions (mechanism, assumption, cost).
Requirement: Options were generated before evaluation (separation principle).
FAIL signal: Options-raw.md contains evaluative language ("This is the best because...", "This won't work because...") mixed with generation content.
PASS signal: options-raw.md is purely descriptive; evaluation appears only in taste-evaluation.md.
Requirement: The HMW question doesn't embed a solution.
FAIL signals:
PASS signal: HMW question can be answered by options that don't share the same technology or UI pattern.
Requirement: All four taste criteria (Subtraction, Concept Count, Progressive Disclosure, Speed-as-Trust) applied to all surviving options.
FAIL signals:
PASS signal: Full scoring matrix present for all post-DVF-filter options with all criteria scored.
Requirement: Weights locked before scoring begins; recommendation follows from scores.
FAIL signals:
PASS signal: Recommended option has highest or second-highest weighted total; if second-highest, reason for not recommending top is documented.
Requirement: Scores justified against rubric, not assigned freely.
FAIL signals:
PASS signal: Each score accompanied by one sentence referencing the specific rubric level.
Requirement: Recommendation traceable to JTBD → Research → Scores.
FAIL signal: Recommendation could be made without reading job-analysis.md or taste-evaluation.md.
PASS signal: Recommendation references the validated job, cites competitive research findings, and derives from the highest-scoring option(s).
Requirement: "Runner-up" case documented — which option almost won and why.
FAIL signal: Only the winning option discussed in recommendation.
PASS signal: recommendation.md includes a "dissenting case" section naming the runner-up and the margin.
Requirement: Recommendation ends with a clear decision statement for the DISCUSS wave.
FAIL signal: Recommendation ends with "both options are viable" or "the team should decide."
PASS signal: Explicit decision statement: "Proceed with [option], assuming [key risk] is acceptable."
review_result:
artifact_path: "docs/feature/{id}/diverge/"
review_date: "{timestamp}"
reviewer: "nw-diverger-reviewer"
jtbd_rigor:
status: "PASSED|FAILED"
issues: [{check, location, quoted_evidence, remediation}]
research_quality:
status: "PASSED|FAILED"
issues: [{check, location, quoted_evidence, remediation}]
option_diversity:
status: "PASSED|FAILED"
issues: [{check, location, quoted_evidence, remediation}]
taste_application:
status: "PASSED|FAILED"
issues: [{check, location, quoted_evidence, remediation}]
recommendation_coherence:
status: "PASSED|FAILED"
issues: [{check, location, quoted_evidence, remediation}]
approval_status: "approved|conditionally_approved|rejected_pending_revisions"
blocking_issues: []
recommendations: []
Approval thresholds:
approved: all dimensions PASSEDconditionally_approved: no FAILED dimensions, minor issues onlyrejected: any dimension FAILED, with specific remediation requiredAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Take nwave-ai/nw-diverger-review-criteria 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.