Classify VP features into Kano categories and output JSON-LD.
npx skills add https://github.com/ajrmooreuk/pfi-w4m-dev --skill pfc-kano
Please help me install this Agent Skill. Skill: pfc-kano Source: https://github.com/ajrmooreuk/pfi-w4m-dev/tree/main/pfc-core/skills/pfc-kano Command: npx skills add https://github.com/ajrmooreuk/pfi-w4m-dev --skill pfc-kano
Kano analysis helps product teams turn VP features into a prioritized set of categories (Must-Be, Performance, Attractive, Indifferent, Reverse) with decay tracking and PMF signal enrichment, aligning VP and PMF for sharper investment decisions. Follows KANO-ONT v1.0.0 and supports a parallel analytical lens to enrich VP→PMF accuracy.
Invoke the skill with a VP instance name to classify VP features using Kano analysis and generate a Kano JSON-LD output.
High-intent search queries and answers about installing and using this skill.
Kano analysis prioritizes features by classifying them into Must-Be, Performance, Attractive, Indifferent, or Reverse categories based on VP instance data. This links directly to PMF signals, generating a structured FeaturePriority output for sharper investment decisions.
Feature decay projections are forecast by loading prior Kano history alongside current VP and PMF data. This historical context enables the system to assess non-linear satisfaction curves and project future feature category evolution.
Invoke the skill with a VP instance name to classify features and automatically generate a structured KanoClassification, KanoSurvey, and KanoDecay JSON-LD output. This requires no external data beyond the VP and PMF inputs.
Yes, Kano classification integrates PMF signal enrichment to align with downstream KPIs. By combining ICPs and competitive context with VP instance data, it produces segment-aware recommendations tailored for roadmap planning.
Yes, you can load optional prior Kano history to assess decay and forecast future classifications using only VP and PMF inputs. The analysis requires no external data beyond these provided VP and PMF datasets.
Kano analysis is not suitable when you lack VP instance or PMF signal data, as these are required to classify features and map non-linear satisfaction curves. It also requires optional prior Kano history to accurately project decay.
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