Agent Skill
Implementing Llms Litgpt skill
implementing-llms-litgptOrchestra-Research/AI-Research-SKILLs01-model-architecture/litgpt/SKILL.md
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
always on
76 tokname + description
once used
3,065 tokSKILL.md body
bundled files
445 KB
repo stars
14kGitHub
Install the Implementing Llms Litgpt skill
Skills CLI (Claude Code, Codex, Cursor and others)
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill implementing-llms-litgpt
Claude Code, by hand (all projects)
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git /tmp/AI-Research-SKILLs mkdir -p ~/.claude/skills cp -R /tmp/AI-Research-SKILLs/01-model-architecture/litgpt ~/.claude/skills/implementing-llms-litgpt
For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.
What it costs your context
Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.
| When | What loads | Cost |
|---|---|---|
| Every session | Name and description | 76 tok |
| When the agent uses it | The SKILL.md instructions | 3,065 tok |
| Only if opened | 4 bundled files (45 KB) | varies |
Until it is used, implementing-llms-litgpt costs about 18× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 98 skills from Orchestra-Research/AI-Research-SKILLs would load 6,174 tokens of descriptions. Skills vs MCP study
Checks
- The name doesn't match its folder name, as the specification requires.
Checked 2026-10-22 · method
What's inside
The headings of its SKILL.md. Read the full file on GitHub.
- LitGPT - Clean LLM Implementations
- Quick start
- Common workflows
- Workflow 1: Fine-tune on custom dataset
- Workflow 2: LoRA fine-tuning on single GPU
- Workflow 3: Pretrain from scratch
- Workflow 4: Convert and deploy model
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
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