embeddings-runtime-stinger

embeddings-runtime-stinger is a RAG & retrieval skill for AI agents, published by legioncodeinc in the that-git-life repository on GitHub. Skillspace rates embeddings-runtime-stinger C (74/100) and it passed the safety scan; the rating is based on static analysis only.

The embeddings runtime for Hivemind - the @huggingface/transformers + nomic-embed-text-v1.5 (768-dim, q8) daemon that generates vectors for Deep Lake recall. Covers daemon lifecycle (warmup, batching, socket IPC, crash recovery), the NDJSON Unix-socket protocol, embedding model and quantization selection scoped to Hivemind, the embeddings-on vs BM25-fallback decision, local-vs-hosted inference tradeoffs, and the dim-must-match-schema constraint (EMBEDDING_DIMS=768 ties to the FLOAT4[] columns). Use when the user says "should I turn embeddings on", "swap the embedding model", "the embed daemon is stuck", "why is recall falling back to BM25", "change the embedding dimension", or "is 600MB worth the semantic lift". Do NOT use for the Deep Lake dataset schema-heal mechanics themselves (deeplake-dataset stinger), API key security (security-worker-bee), or PRD authorship (library-worker-bee).

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

72

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded C with a clean safety verdict, but our quality filters exclude it: a model that read it found it written only for the repository it lives in. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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