nanoGPT — train GPT models from scratch with minimal code

Prerequisites: Python with PyTorch and GPU access for real training — the skill is a training guide the agent follows, not software Install "nanoGPT — train GPT models from scratch with minimal code" for me. It gives my agent @davila7's nanoGPT walkthrough: architecture (transformer blocks, attention, embeddings), training pipeline, hyperparameter guidance, sampling and generation, fine-tuning, and scaling guidance with practical commands. NOTE: real training needs serious GPU compute — budget for hardware separately; the nanoGPT repo evolves, so verify commands against the current repo. MIT-licensed. Repository: https://github.com/davila7/claude-code-templates/blob/main/cli-tool/components/skills/ai-research/model-architecture-nanogpt/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "nanogpt". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. set up the GPU environment and tell the agent whether to train from scratch or fine-tune; nothing else — it's a training guide). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.