# regen-compute

> regen-compute — cshear-regen-compute. Use this tool when you need to estimate and offset the environmental impact of AI model training sessions. It solves the problem of carbon footprint tracking and ecocredit management for AI applications, providing an interface for inputting session data and outputting verified ecocredits on the Regen Network blockchain. Ideal for use cases where sustainable AI practices and transparent energy accounting are required.

Canonical page: https://skillsregistry.net/skills/cshear-regen-compute  
JSON: https://api.skillsregistry.net/v1/skills/cshear-regen-compute

## Description

Enables AI coding assistants to estimate their session's energy footprint and retire verified ecocredits on Regen Network, with immutable on-chain proof.

## Trust

- **Trust score (0–1):** 0.52
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/nufcb2fnpp)
- **Repository:** <https://github.com/regen-network/regen-compute>

## 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": "cshear-regen-compute"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cshear-regen-compute` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cshear-regen-compute/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