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media-recommender

Generates profile-driven recommendations and rankings for games, manga, anime, films, and TV.

Updated Aug 16, 2026
Search Tags:#personalization#recommendation#games#anime#taste profile#fit scoring#media ranking
One-click install
npx skills add https://github.com/Aveer/skills --skill media-recommender-aveer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: media-recommender
Source: https://github.com/Aveer/skills/tree/main/skills/media-recommender
Command: npx skills add https://github.com/Aveer/skills --skill media-recommender-aveer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve? Generic recommendation lists ignore individual taste, confuse critical prestige with personal fit, and overlook which medium, version, or platform actually suits a specific person. This Skill separates the reusable recommendation engine from a user's taste profile so recommendations stay explainable and calibrated. ## Core Features & Use Cases - Profile-Driven Scoring: Evaluates concept fit, execution confidence, and medium/path fit separately using weights read from a supplied taste profile rather than fixed universal rules. - Domain Overlays: Applies specific guidance for games, manga/anime/visual novels, and films/TV, including adaptation completeness, platform state, and version selection. - Calibration & Anti-Overfitting: Updates taste profiles conservatively from user reactions, separating durable preferences from one-off anecdotes. - Use Case: A user asks which of five manga series to start next. The Skill loads their taste profile, compares candidates on the same dimensions, and returns a ranked shortlist with fit mechanisms, confidence levels, and spoiler-free risk notes. ## Quick Start Ask the assistant to recommend which game or anime to start next based on your taste profile, and it will produce a ranked, explainable shortlist.

Frequently Asked Questions about media-recommender

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get personalized anime or game recommendations from an AI?▼

Provide a taste profile describing your preference dimensions and calibration examples, then ask for a fit check, comparison, or ranked shortlist. The Skill evaluates concept fit, execution confidence, and medium fit separately and explains the dominant fit mechanisms and main risk for each candidate.

How to choose between manga, anime, and visual novel versions of a title?▼

The domain overlay compares adaptation completeness, divergence, audiovisual strengths, reading friction, and route dependence for VNs. It recommends a practical path such as anime only, anime then manga continuation, or sampling one medium first based on your profile.

Does the recommendation workflow need a taste profile to work?▼

A supplied taste profile gives the deepest personalization, but if none exists the Skill builds a provisional one from explicit preferences using the profile template. It flags thin evidence rather than pretending the recommendation is deeply personalized.

Can recommendations account for regional streaming availability?▼

Yes, when availability materially affects the decision the Skill verifies current facts live and distinguishes confirmed access, official listings with uncertain region entitlement, and unverified claims. Web access is recommended for release status, patches, DLC, and platform support.

Why does the Skill avoid numeric scores in some rankings?▼

Confidence reflects evidence quality, not enthusiasm, so weak evidence produces score ranges or qualitative bands instead of false decimal precision. Rankings also weigh commitment, friction, availability, and variety rather than blindly sorting a scalar score.

Does the media recommendation workflow avoid spoilers?▼

Yes, spoiler avoidance is a core rule. The Skill discusses hooks, structure, design, tone, and risks without revealing major late twists, deaths, identities, or endings unless the user explicitly requests them.

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