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 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 expl
Profile →npx skills add waybarrios/opencode-power-pack --skill huggingface-best --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 f
- Step 1: Parse the request
- Device → max parameter budget
- Step 2: Find relevant benchmark datasets
- Step 3: Fetch top models from leaderboards
- Step 4: Enrich with model metadata
- Step 5: Filter and rank
- Step 6: Output
- Comparison table
- Follow-up
- Error handling
export HF_AUTH="Bearer $(cat ~/.cache/huggingface/token)"
curl -s -H "Authorization: $HF_AUTH" \
REST API
CLI (hf-cli)
hf models info org/model1 --json | jq '{safetensors, tags, cardData}'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 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 expl
How do I install it?
Run `npx skills add waybarrios/opencode-power-pack --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 waybarrios/opencode-power-pack, a repository with 443 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.