# sight-cache

> sight-cache — catwithlover-sight-cache. Use this tool when you need to enhance visual memory for AI agents, solving problems related to image recognition and recall. It provides a scalable infrastructure for storing and retrieving visual data, accepting image inputs and outputting relevant information. Ideal for applications requiring efficient visual memory management, such as computer vision and machine learning models.

Canonical page: https://skillsregistry.net/skills/catwithlover-sight-cache  
JSON: https://api.skillsregistry.net/v1/skills/catwithlover-sight-cache

## Description

Visual memory infrastructure for AI agents, built on Cloudflare and MCP.

## Trust

- **Trust score (0–1):** 0.37
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/catwithlover/sight-cache)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "catwithlover-sight-cache"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/catwithlover-sight-cache` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/catwithlover-sight-cache/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt