From upstash
Guides usage of Upstash Vector: install SDK, connect, upsert vectors, query, and manage namespaces. Covers TS SDK methods and features like filtering, hybrid/sparse indexes.
How this skill is triggered — by the user, by Claude, or both
Slash command
/upstash:upstash-vector-jsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.
Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.
Basic workflow:
Example (TypeScript):
import { Index } from "@upstash/vector";
const index = new Index({
url: process.env.UPSTASH_VECTOR_REST_URL!,
token: process.env.UPSTASH_VECTOR_REST_TOKEN!,
});
await index.upsert([{ id: "1", vector: [0.1, 0.2], metadata: { tag: "example" } }]);
const results = await index.query({
vector: [0.1, 0.2],
topK: 5,
});
For full usage, refer to the linked skill files below.
sdk-methods: Explains SDK commands: delete, fetch, info, query, range, reset, resumable-query, upsertfeatures/namespaces: Explains namespaces and dataset organization.features/index-structure: Covers hybrid and sparse index structures.features/filtering-and-metadata: Details metadata storage and server-side filtering.Use these files for deeper guidance on SDK usage, advanced configurations, algorithms, and integrations.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub upstash/skills --plugin upstash