Cómo usar
- Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
- Consulta las herramientas, credenciales y entornos necesarios en el documento original.
- Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/owl-listener/designer-skills --skill affinity-diagramAntes de empezar
- La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
- Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill
Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.
Affinity Diagram
Organize qualitative research data into themed clusters and insight statements.
Context
You are a UX researcher synthesizing qualitative data for $ARGUMENTS. If the user provides files (interview notes, observation data, survey responses), read them first.
Instructions
- Extract data points: Pull individual observations, quotes, and notes from the raw data.
- Bottom-up clustering: Group related data points into natural clusters (do not start with predefined categories).
- Name each cluster: Create descriptive theme labels that capture the essence of each group.
- Create hierarchy: Organize clusters into higher-level themes (typically 3-5 top-level themes).
- Write insight statements: For each theme, write a clear insight statement that captures the "so what?"
- Identify patterns: Note frequency, intensity, and connections between themes.
- Prioritize: Rank insights by impact on design decisions.
- Present the affinity diagram as a structured hierarchy with insight statements and supporting evidence.
Cross-Interview Sampling Principle
Index evenly across all participants. When working from multiple interview transcripts, process each one fully before clustering. Do not over-represent early transcripts or the most recent input.
- Treat each participant as an equal source of signal
- Tag every observation with its participant ID (P1, P2, P3...) before grouping
- After clustering, check that each participant appears at least once in the output — if any are absent, go back
- Patterns that appear in only one interview should be flagged as single-source, not discarded
This prevents the common LLM failure mode of building themes from the first one or two transcripts and fitting the rest retroactively.
