How do I install the Agentuity CLI Cloud Vector Search skill?

Run npx skillmds add majiayu000/agentuity-cli-cloud-vector-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agentuity CLI Cloud Vector Search safe to use?

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Which AI agents work with Agentuity CLI Cloud Vector Search?

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Is Agentuity CLI Cloud Vector Search free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Agentuity CLI Cloud Vector Search?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agentuity CLI Cloud Vector Search by majiayu000

Name: Agentuity CLI Cloud Vector Search
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

Cloud Vector Search

Search for vectors using semantic similarity

Prerequisites

Authenticated with agentuity auth login
Project context required (run from project directory or use --project-id)

Usage

agentuity cloud vector search <namespace> <query> [options]

Arguments

Argument	Type	Required	Description
`<namespace>`	string	Yes	-
`<query>`	string	Yes	-

Options

Option	Type	Required	Default	Description
`--limit`	number	Yes	-	maximum number of results to return (default: 10)
`--similarity`	number	Yes	-	minimum similarity threshold (0.0-1.0)
`--metadata`	string	Yes	-	filter by metadata (format: key=value or key1=value1,key2=value2)

Examples

Search for similar products:

bunx @agentuity/cli vector search products "comfortable office chair"

Search knowledge base:

bunx @agentuity/cli vector list knowledge-base "machine learning"

Limit results:

bunx @agentuity/cli vector search docs "API documentation" --limit 5

Set minimum similarity:

bunx @agentuity/cli vector search products "ergonomic" --similarity 0.8

Filter by metadata:

bunx @agentuity/cli vector ls embeddings "neural networks" --metadata category=ai

Output

Returns JSON object:

{
  "namespace": "string",
  "query": "string",
  "results": "array",
  "count": "number"
}

Field	Type	Description
`namespace`	string	Namespace name
`query`	string	Search query used
`results`	array	Search results
`count`	number	Number of results found

---name: agentuity-cli-cloud-vector-searchdescription: Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operationslicense: Apache-2.0--- # Cloud Vector Search Search for vectors using semantic similarity ## Prerequisites - Authenticated with `agentuity auth login` - Project context required (run from project directory or use `--project-id`) ## Usage ```bash agentuity cloud vector search <namespace> <query> [options] ``` ## Arguments | Argument | Type | Required | Description | |----------|------|----------|-------------| | `<namespace>` | string | Yes | - | | `<query>` | string | Yes | - | ## Options | Option | Type | Required | Default | Description | |--------|------|----------|---------|-------------| | `--limit` | number | Yes | - | maximum number of results to return (default: 10) | | `--similarity` | number | Yes | - | minimum similarity threshold (0.0-1.0) | | `--metadata` | string | Yes | - | filter by metadata (format: key=value or key1=value1,key2=value2) | ## Examples Search for similar products: ```bash bunx @agentuity/cli vector search products "comfortable office chair" ``` Search knowledge base: ```bash bunx @agentuity/cli vector list knowledge-base "machine learning" ``` Limit results: ```bash bunx @agentuity/cli vector search docs "API documentation" --limit 5 ``` Set minimum similarity: ```bash bunx @agentuity/cli vector search products "ergonomic" --similarity 0.8 ``` Filter by metadata: ```bash bunx @agentuity/cli vector ls embeddings "neural networks" --metadata category=ai ``` ## Output Returns JSON object: ```json { "namespace": "string", "query": "string", "results": "array", "count": "number" } ``` | Field | Type | Description | |-------|------|-------------| | `namespace` | string | Namespace name | | `query` | string | Search query used | | `results` | array | Search results | | `count` | number | Number of results found |

Frequently asked questions