huggingface-best
GitHub根据用户任务和设备硬件约束，查询HuggingFace官方基准排行榜，筛选并对比推荐最适合的AI模型。
Trigger Scenarios
Install
npx skills add sediman-agent/OpenSkynet --skill huggingface-best -g -y
SKILL.md
Frontmatter
{
"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:
- 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
safetensorsis 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:
- Remove models exceeding the fp16 parameter budget for the device
- Flag models that fit only with Q4 quantization (multiply budget by ~4 for Q4 capacity)
- 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:
- 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_searchwithfilters=["<task>"]sorted bytrendingScore - All leaderboards fail: fall back to
hub_repo_searchfor popular models tagged with the task, note that results are by popularity rather than benchmark score
Version History
- c9d8953 Current 2026-07-05 19:55
