# Goldilocks

> Use this tool when you need to automate k-point generation for Quantum ESPRESSO calculations, solving convergence reliability issues and optimizing computational parameters. It takes crystal structure files as input and outputs optimal k-point grids, using machine learning models like ALIGNN and Random Forest with configurable confidence levels. Ideal for density functional theory calculations requiring efficient and accurate k-point spacing estimation.

Canonical page: https://skillsregistry.net/skills/stfc-goldilocks  
JSON: https://api.skillsregistry.net/v1/skills/stfc-goldilocks

## Description

Provides k-point generation tools for Quantum ESPRESSO density functional theory calculations using machine learning models. Offers two main functions: estimating optimal k-point spacing with uncertainty quantification and generating k-point grids based on crystal structure files. Supports both ALIGNN and Random Forest models with configurable confidence levels for automated determination of computational parameters while ensuring convergence reliability.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/stfc-goldilocks)
- **Repository:** <https://github.com/stfc/goldilocks-mcp>

## 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": "stfc-goldilocks"
    }
  }
}
```

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

---
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