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MeridianVega
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librarian-search

Rank candidate movies and TV releases using TMDB, Jellyfin, and Prowlarr data.

Updated Aug 27, 2026
Search Tags:#releases#dedupe#librarian#tmdb#taste#prowlar
One-click install
npx skills add https://github.com/MeridianVega/claude-marketplace --skill librarian-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: librarian-search
Source: https://github.com/MeridianVega/claude-marketplace/tree/main/plugins/ersatztv-programmer/skills/librarian-search
Command: npx skills add https://github.com/MeridianVega/claude-marketplace --skill librarian-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates candidate discovery, deduplication against Jellyfin records, and release selection for librarian tasks by integrating TMDB data, taste scoring, and Prowlarr releases.

Core Features & Use Cases

  • Discover candidates from TMDB and pull metadata for ranking; deduplicate against an existing Jellyfin library using TMDB/IMDB IDs; score candidates against a taste profile; search releases with Prowlarr and pick the best per candidate using a transparent scoring formula.
  • Handle both movies and TV shows with a multi-stage pipeline (discover → dedupe + score → release-select) to deliver ready-to-queue results.
  • Use case: A librarian user provides a request like "20 mid-budget 80s horror films" and the skill returns a ranked list of candidate titles with corresponding releases.

Quick Start

Provide a need like "20 mid-budget thrillers from the 1980s" and the skill will produce a ranked list of candidate releases.

Frequently Asked Questions about librarian-search

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

FAQPage Schema
How do I discover and rank media candidates for a librarian using TMDB and Jellyfin?▼

To discover and rank media, provide a request like "20 mid-budget 80s horror films" and the skill pulls TMDB metadata, deduplicates against Jellyfin records using TMDB/IMDB IDs, and scores results against a taste profile to deliver ranked candidates.

What's the best way to deduplicate TMDB movie and TV candidates against an existing Jellyfin library?▼

The best way to deduplicate candidates is matching TMDB and IMDB IDs from newly discovered media against existing Jellyfin library records, ensuring no duplicate movies or TV shows enter the ranked queue.

Can I use Prowlarr to search and select releases for both movies and TV shows?▼

Yes, you can use Prowlarr to search and select releases for both movies and TV shows, picking the best available release per candidate using a transparent scoring formula after deduplication and taste scoring.

Do I need a Prowlarr API key and TMDB token to automate release selection for my media library?▼

Yes, you need a PROWLARR_API_KEY and TMDB_API_TOKEN in your environment, along with a taste.md file, to automate discovery, deduplication, taste-based scoring, and release selection for your media library.

How does taste-based scoring work when ranking discovered media items?▼

Taste-based scoring evaluates discovered media items against a taste.md profile, ranking candidates by matching their TMDB metadata attributes against defined preferences before searching Prowlarr for releases.

What limitations exist when using Prowlarr and Jellyfin for media deduplication and release selection?▼

Limitations include relying entirely on TMDB and IMDB IDs for Jellyfin deduplication accuracy and requiring valid PROWLARR_API_KEY and TMDB_API_TOKEN environment variables to produce ready-to-queue release results.

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