Ingest and index multimedia assets with vector embeddings for semantic search.
npx skills add https://github.com/rkz91/coco --skill meta-media
Please help me install this Agent Skill. Skill: meta:media Source: https://github.com/rkz91/coco/tree/main/skills/media-memory Command: npx skills add https://github.com/rkz91/coco --skill meta-media
Multimodal media memory addresses the challenge of losing context across diverse assets by enabling persistent ingestion, embedding, and fast recall of images, video, audio, and documents.
Ingest a media item and then run a search to retrieve related images or clips from memory.
High-intent search queries and answers about installing and using this skill.
Semantic search across multimedia assets works by matching natural language queries against stored vector embeddings and metadata filters to retrieve relevant images, video, audio, and documents from memory.
To ingest and index multimedia assets, store the media with rich metadata and generate vector embeddings to enable fast semantic recall and search across your digital archive.
Yes, managing digital assets semantically requires a local vector store and embedding model to store, index, and retrieve multimedia files using semantic and metadata filters.
The best way to retrieve past media for brand compliance and archival research is using multimodal memory to persistently ingest and semantically recall diverse assets quickly.
Yes, you can filter media search results by recency and metadata when recalling assets, combining natural language queries with specific metadata constraints to retrieve relevant files.
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handsomelong922Store and retrieve embeddings with metadata using a vector database.
