Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/huggingface-best
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on
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Stars10,858
Forks722
LanguagePython
LicenseApache-2.0
📦 Ships with huggingface-skills
</> SKILL.md
huggingface-best.SKILL.md
---name: huggingface-best
description: >
Use when the user asks about finding the best, top, or recommended model for a task,
wants to know what AI model to use, or wants to compare models by benchmark scores.
Triggers on: "best model for X", "what model should I use for", "top models for [task]",
"which model runs on my laptop/machine/device", "recommend a model for", "what LLM should
I use for", "compare models for", "what's state of the art for", or any question about
choosing an AI model for a specific use case. Always use this skill when the user wants
model recommendations or comparisons, even if they don't explicitly mention HuggingFace
or benchmarks.
---# 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:
Read the returned list and select the datasets most relevant to the user's task — match on dataset id, tags, and description. Use your judgment; don't limit yourself to 2-3. Aim for comprehensive coverage: if 5 benchmarks clearly cover the task, use all 5.
hf models info org/model1 --json | jq '{safetensors, tags, cardData}'
```
Extract from each response:
- **Parameters**: `safetensors.total` → convert to B (e.g., 7_241_748_480 → "7.2B")
- **License**: from model card tags (look for `license:apache-2.0`, `license:mit`, etc.)
- If `safetensors` is absent, parse size from the model name (look for "7b", "8b", "13b", "70b", "72b", etc.)
---
## Step 5: Filter and rank
**If a device was specified:**
1. Remove models exceeding the fp16 parameter budget for the device
2. Flag models that fit only with Q4 quantization (multiply budget by ~4 for Q4 capacity)
3. If a highly-ranked model is slightly over budget, keep it with a "needs Q4" note — don't silently drop it
**If no device was mentioned:** skip all size filtering — just rank by benchmark score.
Then: rank by benchmark score (descending), keep top 5-8 models.
Include proprietary models (GPT-4, Claude, Gemini) if they appear on leaderboards, but flag them as "API only / not self-hostable". If the user explicitly asked for local/open models only, exclude them.
---
## Step 6: Output
### Comparison table
```markdown
| # | Model | Params | [Benchmark 1] | [Benchmark 2] | License | On device |
After presenting the table, ask the user: "Would you like to run **[top recommended model]**?"
If they say yes, ask whether they'd prefer to:
- **Run locally** — ask about their device if not already known, then give appropriate setup instructions
- **Run on HF Jobs** — point them to the HF Jobs guide: https://huggingface.co/docs/huggingface_hub/en/guides/jobs
---
## Error handling
- **Leaderboard not found**: skip, note "leaderboard unavailable" in output
- **Model missing from hub_repo_details**: fall back to parsing size from model name
- **No benchmarks found for task**: use the curated fallback table above, or try `hub_repo_search` with `filters=["<task>"]` sorted by `trendingScore`
- **All leaderboards fail**: fall back to `hub_repo_search` for popular models tagged with the task, note that results are by popularity rather than benchmark score
