القائمة
Download GGUF models from HuggingFace directly into LM Studio's model directory. Triggers on "download model", "get model", "grab model", "install model".
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Orchestrate LiteBench model testing — scan LM Studio for models, run the agent harness against each, collect scores, swap models, and produce a leaderboard. Triggers on "test models", "run benchmark", "run harness", "test all models", "benchmark models", "start testing".
Autonomous agent and skill trainer — transforms the current Claude session into a self-improving loop inspired by Karpathy's autoresearch. Activates on: "train", "evolve agent", "improve agent", "run training loop", "agent trainer", "evolve this agent", "train this agent", "self-improving agent", "auto-improve", "training loop", "agent evolution", "autotrainer", "improve skill", "evolve skill", "optimize skill", "skill training", "improve my skills". Targets agents (.claude/agents/) OR skills (.claude/skills/) — auto-detects which. For agents: spawns A/B variants, evaluates output quality, mutates config. For skills: uses Claude Code's built-in eval system (run_eval.py) to measure trigger accuracy, then mutates the skill description. Use when you want an agent or skill to improve itself over N cycles without manual intervention.
Tune the agent harness system prompts to improve model tool-calling scores. Triggers on "tune harness", "improve scores", "train harness", "optimize prompts".
| name | model-download |
| description | Download GGUF models from HuggingFace directly into LM Studio's model directory. Triggers on "download model", "get model", "grab model", "install model". |
| type | skill |
Download GGUF model files from HuggingFace into LM Studio's model directory so they can be loaded and tested with LiteBench.
Models go to: ~/.lmstudio/models/<publisher>/<model-name>/
On Windows: C:/Users/<username>/.lmstudio/models/
curl -s "https://huggingface.co/api/models?search=<query>+gguf&limit=10" | python -c "import sys,json; d=json.load(sys.stdin); [print(m['id']) for m in d]"
curl -s "https://huggingface.co/api/models/<repo-id>/tree/main" | python -c "
import sys, json
files = json.load(sys.stdin)
for f in files:
if f.get('path','').endswith('.gguf'):
size_mb = f.get('size', 0) / 1024 / 1024
print(f'{f[\"path\"]:60s} {size_mb:.0f} MB')
"
Prefer Q4_K_M quantization (good balance of quality and size).
mkdir -p ~/.lmstudio/models/<publisher>/<model-GGUF>/
curl -L -o ~/.lmstudio/models/<publisher>/<model-GGUF>/<filename>.gguf \
"https://huggingface.co/<repo-id>/resolve/main/<filename>.gguf"
ls -lh ~/.lmstudio/models/<publisher>/<model-GGUF>/
The model will appear in LM Studio after a rescan. User needs to load it manually in LM Studio's UI.
For tool calling / agent benchmarking:
| Model | Repo | Size | Why |
|---|---|---|---|
| xLAM 2 1B | Salesforce/xLAM-2-1b-fc-r-gguf | ~940MB | Purpose-built for function calling |
| xLAM 2 8B | Salesforce/xLAM-2-8b-fc-r-gguf | ~5GB | #1 on BFCL leaderboard |
| Hermes 3 8B | NousResearch/Hermes-3-Llama-3.1-8B-GGUF | ~5GB | Gold standard structured tool use |
| Llama 3.1 8B | lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF | ~5GB | Meta native tool calling |
| Llama 3.2 3B | lmstudio-community/Llama-3.2-3B-Instruct-GGUF | ~2GB | Smallest Llama with tools |