# LLMling

> Use this tool when you need to manage and integrate Large Language Models (LLMs) with external resources and tools. It solves problems of LLM configuration, data interaction, and workflow automation by providing a declarative framework with YAML configuration files. The LLMling tool takes in YAML configurations and outputs managed LLM resources, enabling seamless interactions with external data, CLI commands, and Python functions.

Canonical page: https://skillsregistry.net/skills/phil65-llmling  
JSON: https://api.skillsregistry.net/v1/skills/phil65-llmling

## Description

A declarative framework that manages LLM resources, prompts, and tools through YAML configuration files. It enables LLMs to interact with external data, CLI commands, and Python functions using the Model Context Protocol.

## Trust

- **Trust score (0–1):** 0.63
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/yp73wce6y9)
- **Repository:** <https://github.com/phil65/LLMling>

## 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": "phil65-llmling"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/phil65-llmling` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/phil65-llmling/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