Installation

1. 1
   Make sure Claude is on your device and in your terminal.

   Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

   One-time setup

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   npm i -g @anthropic-ai/claude-code

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2. 2
   Paste into Claude Code or into your terminal.

   Install

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   git clone ht••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••••••••••••••••••••••• •• ••••• •• •••••••••••••••••••••••••••••••••••••••••• •• •• •• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••••••••••••••••••••••••••••••••

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   This copies the whole skill folder into ~/.claude/skills/llamafactory-prism-shadow/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

   Faster alternative (instruction-only skills)

   Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

   Quick install (SKILL.md only)

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   mkdir -p ~/.••••••••••••••••••••••••••••••••••••••• •• •••• ••••• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• •••••••••••••••••••••••••••••••••••••••••••••••••••

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3. 3
   Restart Claude Code.

   Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

4. 4
   Just ask Claude.

   Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Fine-tune LLMs with LlamaFactory — register datasets, train via YAML configs, merge LoRA adapters and serve the result.

What this skill does

git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
cd LlamaFactory
pip install -e .
pip install -r requirements/metrics.txt   # optional: evaluation metrics

"my_dataset": { "file_name": "my_dataset.json" }

model_name_or_path: Qwen/Qwen3-1.7B
trust_remote_code: true
stage: sft
do_train: true
finetuning_type: lora
lora_rank: 8
lora_target: all
dataset: my_dataset
template: qwen3
output_dir: saves/qwen3-1.7b/lora/sft
learning_rate: 1.0e-4
num_train_epochs: 3.0
bf16: true

llamafactory-cli train my_sft.yaml

model_name_or_path: Qwen/Qwen3-1.7B
adapter_name_or_path: saves/qwen3-1.7b/lora/sft
template: qwen3
trust_remote_code: true
export_dir: saves/qwen3-1.7b-sft-merged

llamafactory-cli export my_merge.yaml

model_name_or_path: Qwen/Qwen3-1.7B
adapter_name_or_path: saves/qwen3-1.7b/lora/sft
template: qwen3
infer_backend: huggingface
trust_remote_code: true

llamafactory-cli chat my_infer.yaml   # interactive chat with the tuned model
llamafactory-cli api my_infer.yaml    # OpenAI-compatible API server