embeddings-runtime-stinger

What problem does it solve?

This Skill helps engineers make reliable decisions about enabling, operating, and evolving Hivemind's local embedding runtime without breaking retrieval quality or vector schemas.

Core Features & Use Cases

• Embedding Runtime Operations: Guides daemon lifecycle management including warmup, batching, Unix-socket IPC, crash recovery, and local inference behavior.
• Model and Configuration Decisions: Evaluates embedding model swaps, quantization choices, local versus hosted inference, and whether semantic search is worth the added resource cost.
• Schema Safety: Protects 768-dimensional vector storage requirements by validating model dimensions and planning migrations when embedding schemas change.
• Use Case: Help a developer decide whether to enable embeddings, diagnose a stuck embedding daemon, or plan a model migration while preserving recall integrity.

Quick Start

Use the embeddings-runtime-stinger skill to analyze whether semantic embeddings should be enabled for my Hivemind setup and provide a recommendation.