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Huggingface Best

Hugging FaceOfficialPublished by Hugging Face

  • Skill
  • Research and analysis
  • Data and AI
  • Apache-2.0
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From the author

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.
— the description by Hugging Face

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill assists users in selecting the most suitable AI models for specific tasks by querying official leaderboard data. It processes user requirements regarding task type and hardware specifications to provide a ranked comparison table. The skill evaluates models based on benchmark scores, parameter counts, and licensing, while offering guidance on whether a model can run locally on the user's device or requires cloud-based execution.

What it does

  • Parses user requests to identify specific AI tasks and hardware constraints.
  • Calculates parameter budgets based on available device memory.
  • Retrieves and aggregates data from official benchmark leaderboards.
  • Enriches model data with parameter counts and license information.
  • Filters and ranks models based on benchmark performance and hardware compatibility.
  • Generates a structured comparison table for easy model evaluation.

When to use it

  • When you need to find the best AI model for a specific task like coding, RAG, or image classification.
  • When you want to compare models based on objective benchmark scores.
  • When you need to determine if a specific model will fit on your local hardware.
  • When you are looking for state-of-the-art models for a particular use case.

Advantages

  • Provides data-driven recommendations based on official benchmark results.
  • Considers hardware limitations to ensure model feasibility.
  • Offers clear visibility into model licensing and size.
  • Simplifies the selection process by aggregating fragmented leaderboard information.

Usage examples

“What is the best model for coding that I can run on my 16GB MacBook?”
The agent identifies top-performing coding models, filters them by your 16GB memory constraint, and provides a comparison table showing which models fit in fp16 or Q4 quantization.
“Compare the top models for speech recognition.”
The agent retrieves the latest benchmark data for speech recognition and presents a ranked table of the top models, including their benchmark scores and license types.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/huggingface/huggingface-best/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip huggingface-best.zip -d .agents/skills

Documentation by Hugging Face

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

Fetch the full list of official HF benchmarks:

curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
  "https://huggingface.co/api/datasets?filter=benchmark:official&limit=500" | jq '[.[] | {id, tags, description}]'

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.


Step 3: Fetch top models from leaderboards

For each selected benchmark dataset:

curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
  "https://huggingface.co/api/datasets/<namespace>/<repo>/leaderboard" | jq '[.[:15] | .[] | {rank, modelId, value, verified}]'

Collect model IDs and scores across all benchmarks. If a leaderboard returns an error (404, 401, etc.), skip it and note it in the output.


Step 4: Enrich with model metadata

For the top 10-15 candidate model IDs, get model infos.

# REST API
curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
  "https://huggingface.co/api/models/org/model1" | jq '{safetensors, tags, cardData}'

# CLI (hf-cli)
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
| # | Model | Params | [Benchmark 1] | [Benchmark 2] | License | On device |
|---|-------|--------|--------------|--------------|---------|-----------|
| ⭐1 | [org/name](https://huggingface.co/org/name) | 7B | 85.2% | — | Apache 2.0 | Yes (fp16) |
| 2 | [org/name](https://huggingface.co/org/name) | 13B | 83.1% | 71.5% | MIT | Q4 only |
| 3 | [org/name](https://huggingface.co/org/name) | 70B | 90.0% | 81.0% | Llama | Too large |
  • Link model names to https://huggingface.co/<model_id>

  • Use — for benchmarks where the model wasn't evaluated

  • Star the top recommended pick with ⭐

  • "On device" values: Yes (fp16), Q4 only, Too large, API only

Follow-up

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:


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

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

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Attribution and licence

Published by
Hugging Face
Publisher
Hugging Face (Official)
Source
huggingface/skills · skills/huggingface-best · commit 25910b3
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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