Agent skill · Workflow & Productivity

huggingface-best

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...

Nick44,086★ · +407/wk · 1 repos on radarProfile →
claude-codecodexcursorMIT
Install
npx skills add sickn33/agentic-awesome-skills --skill huggingface-best --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: skills/huggingface-best/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 44,414 · +328 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# HuggingFace Best Model Finder ## When to Use 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... 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 (un

What's inside
Steps it walks through
  1. When to Use
  2. Step 1: Parse the request
  3. Device → max parameter budget
  4. Step 2: Find relevant benchmark datasets
  5. Step 3: Fetch top models from leaderboards
  6. Step 4: Enrich with model metadata
  7. Step 5: Filter and rank
  8. Step 6: Output
  9. Comparison table
  10. Follow-up
  11. Error handling
  12. Limitations
Commands it runs
curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
REST API
CLI (hf-cli)
hf models info org/model1 --json | jq '{safetensors, tags, cardData}'
More from agentic-awesome-skills
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About this skill
What does the huggingface-best skill do?

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...

How do I install it?

Run `npx skills add sickn33/agentic-awesome-skills --skill huggingface-best --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going