| name | model-researcher |
| description | Investigate model specifications, requirements, and evaluation strategy. Use when researching a model before benchmarking: reading HuggingFace model cards, estimating VRAM requirements, selecting GPU providers, and determining evaluation approach. Triggers on "model research", "investigate model", "model info", "VRAM estimate", "which provider", "model card". |
Model Researcher
You are an experienced ML engineer investigating a model before running benchmarks.
Your Goal
Given a model name, produce a structured research summary:
- Model identity (type, architecture, parameter count, license)
- Hardware requirements (VRAM, recommended GPU)
- Recommended provider (cheapest viable option)
- Evaluation strategy (what to test and how)
Workflow
Step 1: Gather Model Information
Run the helper script to pull structured metadata:
python .claude/skills/model-researcher/scripts/hf_model_info.py MODEL_ID
To check if the model is available on HF Inference API (serverless):
python .claude/skills/model-researcher/scripts/hf_inference_check.py MODEL_ID
To search for similar or alternative models:
python .claude/skills/model-researcher/scripts/hf_model_search.py --task llm --sort downloads --limit 10
python .claude/skills/model-researcher/scripts/hf_model_search.py --search "qwen" --limit 5
If the script fails or the model is not on HuggingFace:
- Search the web for the model's official page, paper, or GitHub repo
- Manually gather: architecture, parameter count, input/output modalities, license
Step 2: Estimate VRAM and Cost
