# REEL-FINDER

> REEL-FINDER — mathewpius19-reel-finder. Use this tool when you need to find personalized recommendations based on your interactions and preferences. REEL-FINDER solves the problem of information overload by providing tailored results to natural language queries, using Sentence Transformers and User Embeddings to understand user behavior. It takes in natural language input and returns relevant recommendations as output, making it ideal for use cases where users need customized suggestions.

Canonical page: https://skillsregistry.net/skills/mathewpius19-reel-finder  
JSON: https://api.skillsregistry.net/v1/skills/mathewpius19-reel-finder

## Description

Full-stack movie discovery and recommendation system with LLM tool calling, semantic search, and interaction-based personalization.

## Trust

- **Trust score (0–1):** 0.85
- **Verification tier:** verified
- **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/mathewpius19/REEL-FINDER)

## 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": "mathewpius19-reel-finder"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mathewpius19-reel-finder` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mathewpius19-reel-finder/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