ux-kano-model
Kano model feature classification and prioritization sub-skill for the /user-experience parent skill. Classifies product features into Must-be (M), Performance (O), Attractive (A), Indifferent (I), and Reverse (R) categories using the functional/dysfunctional questionnaire pair methodology (Kano et al., 1984). Computes Customer Satisfaction (CS) coefficients (Better/Worse) for priority matrix visualization. Produces feature classification reports, priority matrices, and survey design templates. Sample size awareness: 5-8 respondents yields directional classification only (MEDIUM confidence); 20+ respondents required for statistical classification (Berger et al., 1993). Invoked by ux-orchestrator during Wave 4 lifecycle-stage routing or when user intent is "Need to prioritize features" at any lifecycle stage. Triggers: Kano, must-be, attractive, one-dimensional, performance feature, satisfaction, feature classification, delighter, feature prioritization, CS coefficient.
What this skill does
<!-- VERSION: 1.2.0 | DATE: 2026-03-04 | SOURCE: skills/user-experience/SKILL.md | PARENT: /user-experience skill | REVISION: iter4 quality gate revision -- add framework-inference qualifier to bypass rationale (Evidence Quality 0.91->target), add Phase 5 inline template fallback for symmetric treatment with Phase 2 (Actionability 0.93->target), use consistent [PLANNED: Wave 4 Phase 2] token in Quality Gate Integration prose (Traceability 0.95->target) -->
# Kano Model Sub-Skill
> **Version:** 1.2.0
> **Framework:** Jerry User-Experience -- Kano Model
> **Constitutional Compliance:** Jerry Constitution v1.0
> **Parent Skill:** `/user-experience` (`skills/user-experience/SKILL.md`)
> **Wave:** 4 (Advanced Analytics)
> **Project:** PROJ-022 User Experience Skill | GitHub Issue [#138](https://github.com/geekatron/jerry/issues/138)
## Document Sections
| Section | Purpose |
|---------|---------|
| [Document Audience](#document-audience-triple-lens) | Triple-Lens audience guide |
| [Purpose](#purpose) | Sub-skill overview and key capabilities |
| [When to Use This Sub-Skill](#when-to-use-this-sub-skill) | Activation triggers and scope boundaries |
| [Available Agents](#available-agents) | Single agent with role, model, and output location |
| [P-003 Compliance](#p-003-compliance) | Worker agent hierarchy position |
| [Invoking the Agent](#invoking-the-agent) | Invocation via ux-orchestrator |
| [Methodology](#methodology) | Kano Model categories, evaluation table, CS coefficients, sample size considerations |
| [Execution Procedure](#execution-procedure) | 5-phase workflow from scope definition through synthesis |
| [Output Specification](#output-specification) | Output location, L0/L1/L2 structure, required sections |
| [Routing](#routing) | Keywords and lifecycle-stage routing integration |
| [Cross-Framework Integration](#cross-framework-integration) | Upstream inputs and downstream handoffs |
| [Synthesis Hypothesis Confidence](#synthesis-hypothesis-confidence) | Confidence classifications with rationale |
| [Quality Gate Integration](#quality-gate-integration) | H-13/H-14 compliance for C2+ deliverables |
| [Degraded Mode Behavior](#degraded-mode-behavior) | Operation with insufficient respondents or without survey data |
| [Wave Architecture](#wave-architecture) | Wave 4 entry criteria and bypass conditions |
| [Constitutional Compliance](#constitutional-compliance) | Governing principles |
| [Registration](#registration) | Parent-routed registration model |
| [Deployment Status](#deployment-status) | Current implementation status |
| [Quick Reference](#quick-reference) | Common workflows and agent selection hints |
| [References](#references) | Full repo-relative paths to all referenced files |
## Document Audience (Triple-Lens)
This SKILL.md serves multiple audiences:
| Level | Audience | Sections to Focus On |
|-------|----------|---------------------|
| **L0 (Stakeholder)** | Product managers, feature owners | [Purpose](#purpose), [When to Use This Sub-Skill](#when-to-use-this-sub-skill), [Quick Reference](#quick-reference) |
| **L1 (Developer)** | Engineers invoking the agent | [Invoking the Agent](#invoking-the-agent), [Execution Procedure](#execution-procedure), [Output Specification](#output-specification) |
| **L2 (Architect)** | Workflow designers, skill maintainers | [Methodology](#methodology), [Cross-Framework Integration](#cross-framework-integration), [Synthesis Hypothesis Confidence](#synthesis-hypothesis-confidence) |
---
## Purpose
The Kano Model sub-skill provides AI-augmented feature classification and prioritization using the Kano Model methodology (Kano et al., 1984). It assists tiny teams (1-5 people) in understanding which features satisfy, delight, or frustrate users by classifying features into five categories based on the functional/dysfunctional questionnaire pair.
The agent operates on user-provided survey data or assists with survey design when no data exists. It does NOT conduct surveys itself -- it designs questionnaires, analyzes response data, and produces classification reports with Customer Satisfaction (CS) coefficients for priority decision-making.
### Key Capabilities
- **Survey Design** -- Generates functional/dysfunctional question pairs ready for team administration
- **Response Classification** -- Maps answer pairs to Kano categories using the 5x5 evaluation table (Kano et al., 1984)
- **CS Coefficient Calculation** -- Computes Better/Worse coefficients per feature (Berger et al., 1993)
- **Priority Matrix** -- Better vs. Worse scatter plot for feature prioritization
- **Feature Lifecycle Analysis** -- Attractive-to-Performance-to-Must-be migration patterns
- **Conflict Detection** -- Identifies split classifications and flags for domain expert resolution
- **Sample Size Awareness** -- Calibrates confidence by respondent count (5-8 = directional; 20+ = statistical)
---
## When to Use This Sub-Skill
Activate when:
- Prioritizing a backlog of potential features by user satisfaction impact
- Deciding which features are "table stakes" (Must-be) versus differentiators (Attractive)
- Designing a Kano survey questionnaire for team administration
- Analyzing existing Kano survey response data
- Determining which features to invest in, maintain, or deprioritize
- Understanding how feature expectations shift as a product matures (lifecycle dynamics)
- Resolving disagreements about feature priority with a structured, evidence-based framework
- Receiving a job-derived feature list from `/ux-jtbd` that needs prioritization
Do NOT use for:
- General usability evaluation of existing interfaces (use `/ux-heuristic-eval`)
- Diagnosing why users fail to take a desired action (use `/ux-behavior-design`)
- Measuring ongoing UX health metrics (use `/ux-heart-metrics`)
- Accessibility compliance auditing (use `/ux-inclusive-design`)
- Building component libraries (use `/ux-atomic-design`)
- User motivation research without a defined feature list (use `/ux-jtbd`)
- Running rapid prototyping sprints (use `/ux-design-sprint`)
- General research without UX focus (use `/problem-solving`)
---
## Available Agents
| Agent | Role | Tier | Mode | Model | Wave | Output Location |
|-------|------|------|------|-------|------|-----------------|
| `ux-kano-analyst` | Kano model feature classification and prioritization | T2 | Convergent | Sonnet | 4 | `projects/${JERRY_PROJECT}/engagements/{engagement-id}/ux-kano-analyst-{topic-slug}.md` |
**Tool tier:** T2 = Read-Write (Read, Write, Edit, Glob, Grep, Bash). This agent operates on user-provided data only -- it does NOT have WebSearch, WebFetch, or Context7 MCP tools. The T2 assignment follows the principle of least privilege (AR-006): the agent reads feature lists and survey data provided by the user, performs classification analysis, and writes output reports. No external research is required because the Kano methodology is self-contained within the agent's training knowledge.
**Cognitive mode:** Convergent -- narrows from feature list and response data to classified priorities. Each iteration refines rather than expands.
Output at three levels per AD-M-004:
- **L0 (Executive Summary):** Feature classification overview with top priorities for stakeholders and cross-framework synthesis input.
- **L1 (Technical Detail):** Full classification table, CS coefficients, evaluation methodology, conflict analysis, and priority matrix.
- **L2 (Strategic Implications):** Feature lifecycle dynamics, competitive positioning, product maturity trajectory, and roadmap recommendations.
---
## P-003 Compliance
This sub-skill contains a single worker agent (`ux-kano-analyst`) that is invoked by the parent `ux-orchestrator`. The agent MUST NOT include Agent in its tool list per H-01/P-003.
```
MAIN CONTEXT (user request)
|
v
ux-orchestrator (T5, Opus, Integrative) -- routes, gates, synthesizes
|
+-- ux-kano-analyst (T2, Convergent, Sonnet) [Wave 4] -- THIS SUB-SKIRelated in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.