Stats
Actions
Tags
'%20stop-opacity%3D'0.16'%2F%3E%3Cstop%20offset%3D'1'%20stop-color%3D'rgb(200%2C90%2C60)'%20stop-opacity%3D'0.03'%2F%3E%3C%2FlinearGradient%3E%3C%2Fdefs%3E%3Crect%20width%3D'320'%20height%3D'200'%20fill%3D'url(%23g)'%2F%3E%3Ccircle%20cx%3D'250'%20cy%3D'56'%20r%3D'92'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.06'%2F%3E%3Ccircle%20cx%3D'64'%20cy%3D'172'%20r%3D'58'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.05'%2F%3E%3C%2Fsvg%3E)
From omer-metin-skills-for-antigravity-2
Builds AI-powered personalization systems including recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced experiences.
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:ai-personalizationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Builds recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. Analyzes user preferences and interaction data to generate personalized recommendations.
Designs and debugs personalisation and recommendation systems for two-sided trust marketplaces using AWS Personalize. Covers event tracking, schema design, cold start, bias control, and observability.
Implements collaborative filtering, content-based, and hybrid recommendation systems with matrix factorization and deep learning for product recommendations and personalization.