huggingface-best
Finds the best or recommended model for a task, or one that runs on your device, and compares models by benchmark scores.
- Kind
- Skill
- Works with
- Claude Code
- Source
- Hugging Face Skills @ 25910b3d0ba3
- Path
- skills/huggingface-best/SKILL.md
- Jobs
- Planning
- Topics
- Machine learning
Credit
- Made by
- huggingface
- Original
- github.com/huggingface/skills/blob/25910b3d0ba31a2ba102f901cd…
- License
- Apache-2.0
Listed by Skill Manifest; not made by us.
InstallInstall it by hand, following the source.
hf skills add huggingface-best
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/huggingface-best/ (the skill folder)
- Runs scripts
- Yes
- shell block in the text
- Uses the network
- Yes
- curl / wget
- Needs secrets
- None found
- Source revision
- 25910b3d0ba3
- Licence
- Apache-2.0
- Last activity
- 30 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# HuggingFace Best Model Finder Finds the best models for a task by querying official HF benchmark leaderboards, enriching results with model size data, filtering for what fits on the user's device, and returning a comparison table with benchmark scores. --- ## Step 1: Parse the request Extract from the user's message: - **Task**: what they want the model to do (coding, math/reasoning, chat, OCR, RAG/retrieval, speech recognition, image classification, multimodal, agents, etc.) - **Device**: hardware constraints (MacBook M-series 8/16/32/64GB unified memory, RTX GPU with VRAM amount, CPU-only, cloud/no constraint, etc.) If device is not mentioned, skip filtering entirely and return the highest-performing models regardless of size. If the task is genuinely ambiguous, ask one clarifying question. ### Device → max parameter budget When a device is specified, extract its available memory (unified RAM for Apple Silicon, VRAM for discrete GPUs) and apply: - **fp16 max params (B)** ≈ memory (GB) ÷ 2 - **Q4 max params (B)** ≈ memory (GB) × 2 Examples: 16GB → 8B fp16 / 32B Q4 — 24GB VRAM → 12B fp16 / 48B Q4 — 8GB → 4B fp16 / 16B Q4 --- ## Step 2: Find relevant benchmark datasets …