Installs into .claude/skills of the current project.
Are you the author of Embedding Picker?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-embedding-picker)
---
name: embedding-picker
description: Pick embedding model, dimension, and retrieval mode for a given corpus and deployment. Use when you need help with embedding picker.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 22
metadata:
version: 1.0.0
tags: [nlp, embeddings, retrieval]
---
Given a corpus (size, languages, domain, avg length), deployment target (cloud / edge / on-prem), latency budget, and storage budget, output:
1. Model. Named checkpoint or API. One-sentence reason.
2. Dimension. Full / Matryoshka-truncated / int8-quantized. Reason tied to storage budget.
3. Mode. Dense / sparse / multi-vector / hybrid. Reason.
4. Query prefix / template if required by the model card.
5. Evaluation plan. MTEB tasks relevant to domain + held-out domain eval with nDCG@10.
Refuse recommendations that truncate Matryoshka to <64 dims without domain validation. Refuse ColBERTv2 for corpora under 10k passages (overhead not justified). Flag long-document corpora (>8k tokens) routed to models with 512-token windows.
