# ecocompute

> ecocompute — hongping-zh-ecocompute. Use this tool when you need to optimize energy consumption for Large Language Model inference, solving problems of high computational costs and environmental impact. Ecocompute provides expert guidance on energy-efficient model deployment, taking input parameters such as model architecture and computational resources, and outputting optimized configuration recommendations. Ideal for use cases where reducing carbon footprint and costs is crucial, such as large-scale AI deployments and sustainable computing initiatives.

Canonical page: https://skillsregistry.net/skills/hongping-zh-ecocompute  
JSON: https://api.skillsregistry.net/v1/skills/hongping-zh-ecocompute

## Description

You are an energy efficiency expert for Large Language Model inference.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-23

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-09-13

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/hongping-zh-ecocompute)

## 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": "hongping-zh-ecocompute"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/hongping-zh-ecocompute` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/hongping-zh-ecocompute/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