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mlenergy-data

Analyze The ML.ENERGY Benchmark data (LLM and diffusion inference energy/latency/throughput on H100 and B200) using the mlenergy-data Python toolkit.

CodexClaude Code1 Skill

By Jae-Won ChungLicense: Apache-2.05 GitHub starsUpdated 2 months ago

Directory evidence

Runtimes
Codex and Claude Code
Parsed components
1 skill or MCP entry
Source updated
Aug 13, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install mlenergy-data for Codex and Claude Code

Installs for the current user
codex plugin marketplace add IchenDEV/agent-plugin-mkt
codex plugin marketplace upgrade agent-plugin-marketplace
codex plugin add mlenergy-data@agent-plugin-marketplace

Paste and run these commands in a terminal with Codex. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/ml-energy/data

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The repository root is the plugin root.

Plugin files

mlenergy-data/
├── .codex-plugin/plugin.json
├── .claude-plugin/plugin.json
└── skills/mlenergy-data/SKILL.md

Included Skills1

mlenergy-dataskills/mlenergy-data/SKILL.md

Answer natural-language questions about The ML.ENERGY Benchmark (LLM and diffusion inference energy/latency/throughput measurement data on GPUs) using the `mlenergy-data` Python toolkit. Use when the user asks about energy per token, energy per image/video, throughput, ITL latency, power timelines, GPU comparisons, batch-size sweeps, MoE/Dense/Hybrid architecture comparisons, or FP8/BF16/MXFP4 precision comparisons.

Plugin manifests2

.codex-plugin/plugin.json
{
  "name": "mlenergy-data",
  "description": "Analyze The ML.ENERGY Benchmark data (LLM and diffusion inference energy/latency/throughput on H100 and B200) using the mlenergy-data Python toolkit.",
  "author": {
    "name": "Jae-Won Chung",
    "email": "[email protected]"
  },
  "homepage": "https://ml.energy/data",
  "repository": "https://github.com/ml-energy/data",
  "license": "Apache-2.0",
  "skills": "./skills/",
  "interface": {
    "displayName": "ML.ENERGY Data",
    "websiteURL": "https://ml.energy/data"
  }
}
.claude-plugin/plugin.json
{
  "name": "mlenergy-data",
  "displayName": "ML.ENERGY Data",
  "description": "Analyze The ML.ENERGY Benchmark data (LLM and diffusion inference energy/latency/throughput on H100 and B200) using the mlenergy-data Python toolkit.",
  "author": {
    "name": "Jae-Won Chung",
    "email": "[email protected]"
  },
  "homepage": "https://ml.energy/data",
  "repository": "https://github.com/ml-energy/data",
  "license": "Apache-2.0"
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[mlenergy-data on Agent Plugins Marketplace](https://pluginsmp.com/plugins/mlenergy-data)