radarr-recommend

Analyze user preferences and filter Radarr import lists for movie recommendations.

12|3|Updated Apr 6, 2026

Search Tags:#automation #media #entertainment #recommendation #radarr #movies

Ready to run

npx skills add https://github.com/tsukhani/jclaw --skill radarr-recommend

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: radarr-recommend
Source: https://github.com/tsukhani/jclaw/tree/main/skills/radarr-recommend
Command: npx skills add https://github.com/tsukhani/jclaw --skill radarr-recommend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq.

What problem does it solve?

This skill solves the problem of decision fatigue by providing personalized movie recommendations that align with your specific tastes while ensuring you never accidentally download a duplicate.

Core Features & Use Cases

Preference Learning: Automatically builds a user profile based on your favorite genres, directors, and ratings to tailor suggestions.
Smart Filtering: Cross-references trending TMDb data against your existing library and personal exclusions to surface only relevant, unwatched content.
Use Case: When you are looking for something new to watch, the skill analyzes your history and preferences to present a curated list of trending movies, allowing you to trigger a download directly through your Radarr instance.

Quick Start

Ask the assistant to recommend a movie based on your current preferences to see a curated list of trending titles.

Frequently Asked Questions about radarr-recommend

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

FAQPage Schema

How do I get personalized movie recommendations from my Radarr library?▼

Personalized movie recommendations are generated by analyzing your local USER.md preferences and filtering trending TMDb content from Radarr import lists. The system excludes existing library items and avoided genres to surface only relevant, unwatched films.

Can I prevent duplicate movie downloads when using Radarr automation?▼

Yes, duplicate downloads are prevented by cross-referencing trending data against your existing Radarr library. The smart filtering mechanism ensures only unwatched and non-duplicate titles are suggested for download.

What do I need to set up Radarr movie discovery and filtering?▼

You need a running Radarr API instance for integration and a local USER.md file for preference persistence. The jq dependency is also required to process the JSON data from the API.

How does preference learning work for movie suggestions?▼

Preference learning works by automatically building a user profile based on your favorite genres, directors, and ratings. This profile tailors suggestions by excluding avoided genres and matching trending content to your specific tastes.

Does this Radarr recommendation skill support triggering downloads directly?▼

Yes, downloads can be triggered directly through your Radarr instance. Once the skill analyzes your history and presents a curated list of trending movies, you can select a title to initiate the download process.

What are the limitations of using jq for Radarr API movie recommendations?▼

The system relies on jq to parse API responses, meaning complex data transformations are constrained by jq capabilities. Recommendations are also strictly limited to trending TMDb import lists and exclude manually specified avoided genres.