agentuity-cli-cloud-vector-search

Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations

16 stars

Best use case

agentuity-cli-cloud-vector-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations

Teams using agentuity-cli-cloud-vector-search should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agentuity-cli-cloud-vector-search/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/ai-agents/agentuity-cli-cloud-vector-search/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/agentuity-cli-cloud-vector-search/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How agentuity-cli-cloud-vector-search Compares

Feature / Agentagentuity-cli-cloud-vector-searchStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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 |

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