recommendation▌

This skill implements collaborative and content-based recommendation systems with matrix factorization techniques to predict user preferences, increase engagement, and drive conversions through personalized item suggestions.

Recommend Firebox.eu products for a recipient profile (interests, budget, occasion, recipient role). Uses the storefront's Algolia index for facet-filtered scoring (product_tags, categories.level1, gift_gender, price.EUR.default, personalizable) and returns ranked picks with title, price, url, and a rationale grounded in matched facets. Read-only.

Detect and report available system resources for computational tasks, generating strategic recommendations for optimal processing.

Structured canvas for evaluating AI product ideas across outcomes, hypotheses, risks, and positioning. \n \n Synthesizes 10 strategic components: business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, assumptions, PESTEL risks, value justification, success metrics, and next steps \n Designed for AI-specific uncertainty; treats solutions as testable bets rather than commitments, with lightweight \"Tiny Acts of Discovery\" experiments built in \n Outcome-driven fr

$23

This skill provides comprehensive implementation of recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and hybrid approaches to predict user preferences and deliver personalized suggestions.