MCP App StoreMCP App Store
from nwave564
Checks rigor and quality of DIVERGE-wave artifacts: JTBD extraction, research coverage, option diversity, taste scoring, and recommendation traceability.
This skill provides a structured reviewer for DIVERGE-wave design artifacts. It codifies checks across five dimensions (JTBD rigor, research quality, option diversity, taste application, and recommendation coherence) and produces a YAML-style review result that highlights failures, evidence, and remediation steps. Use it to adversarially validate job-analysis.md, competitive-research.md, options-raw.md, taste-evaluation.md, and recommendation.md before committing to a design direction.
Run this reviewer during the DIVERGE wave to catch weak or incomplete artifacts early. Apply it when a feature team finishes initial job extraction, competitive research, or option generation and before DISCUSS/DECIDE. It's especially useful for teams practicing Lean UX or outcome-driven design who need traceable recommendations.
Works best with agents or tools used for design review and document linting (Claude Code, Codex-style assistants, and local CLI reviewers). It is a non-invocable reviewer intended to be run inside automated pipelines or by human-in-the-loop agents.
This is a review criteria skill (no executable scripts) for the nWave DIVERGE wave process. It defines 5 review dimensions (JTBD rigor, research quality, option diversity, taste application, recommendation coherence) with detailed fail/pass signals and a YAML output schema. Purely declarative — no code to run, no security surface. Well-written and thorough for its niche.
Criteria-only skill with no scripts. Frontmatter correctly marks user-invocable as false and disable-model-invocation as true. The skill is well-structured with clear dimensional checks, specific fail/pass signals, and a formal output schema. Limited audience but high quality within its domain.
nWave Deliver Wave Orchestration
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nWave Agent Creation Workflow
A practical five-phase workflow for designing, building, validating, and refining agent definitions and skills.
nWave: Start a New Feature
Conversational wizard that helps you describe a feature, derives a project ID, recommends the right development wave (discover → discuss → design → deliver), an
Outcome KPI Framework
A practical framework for writing measurable outcome-focused KPIs for features and user stories, mapping outcomes to leading indicators and measurement plans.
arxiv2md
Convert arXiv papers into clean, LLM-ready Markdown via a REST API or CLI for fast reading, summarization, and downstream processing.
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Introduce purposeful color into monochromatic or dull interfaces to improve hierarchy, meaning, and emotional warmth while preserving accessibility.
Shopify Store Audit
Structured, page-by-page audit of Shopify storefronts to find conversion blockers and prioritized fixes for copy, UX, trust signals, and checkout friction.
Scite Database (Smart Citations)
Query Scite.ai Smart Citations by DOI to report supporting, contrasting, and mentioning citation counts and help assess a paper's reliability.
Writing Implementation Plans
Generate detailed, engineer-ready implementation plans from design docs: file paths, step-by-step tasks, tests, and verification instructions.
NotebookLM Research Agent
End-to-end research-to-content pipeline: ingest sources into Google NotebookLM, run deep cited research, generate downloadable artifacts, and produce polished c
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Generate a structured, bilingual daily briefing of the latest AI industry news, model updates, papers, and open-source tools with in-depth technical analysis an