plugins/ml-intern/.codex-plugin/plugin.json

{
  "name": "ml-intern",
  "version": "0.1.3",
  "description": "Hugging Face ML Intern for Codex — research ML papers first, inspect models and datasets, run training and evaluation jobs, and ship ML artifacts.",
  "author": {
    "name": "Hugging Face",
    "email": "agents@huggingface.co",
    "url": "https://github.com/huggingface"
  },
  "homepage": "https://huggingface.co/docs/hub/agents-skills",
  "repository": "https://github.com/razvan/ml-intern-codex-plugin",
  "license": "Apache-2.0",
  "keywords": ["huggingface", "ml", "machine-learning", "training", "fine-tuning", "evaluation", "datasets", "models"],
  "skills": "./skills/",
  "interface": {
    "displayName": "ML Intern",
    "shortDescription": "Hugging Face ML engineering agent for Codex",
    "longDescription": "ML Intern is an autonomous ML engineering agent for the Hugging Face ecosystem. It follows a strict research-first workflow: clarify the deliverable, search papers first for novel or paper-backed tasks, trace citations, inspect likely datasets, read current HF docs and GitHub examples, use web sources only when necessary, validate datasets and models, and only then write code, run HF Jobs, and ship ML artifacts.",
    "developerName": "Hugging Face",
    "category": "Coding",
    "capabilities": ["Interactive", "Read", "Write"],
    "websiteURL": "https://huggingface.co",
    "defaultPrompt": [
      "Research a paper-backed ML task and return a plan only",
      "Fine-tune a language model on a custom dataset using Hugging Face Jobs",
      "Find the best open-source embedding model and benchmark it"
    ],
    "brandColor": "#FF6B00"
  }
}