New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Huggingface Best

by waybarrios in waybarrios/opencode-power-pack

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Huggingface Best

skills CLI
$ npx skills add waybarrios/opencode-power-pack --skill huggingface-best -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install waybarrios/opencode-power-pack huggingface-best --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/waybarrios/opencode-power-pack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/huggingface-best .claude/skills/huggingface-best && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
huggingface-best
GitHub stars
534
Token cost
~1.4k tokens
SKILL.md length
594 words
Files
1
Skills in repo
32
Repo updated
First seen
Licence
Apache-2.0

At a glance

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints.

  • Works in 6 steps: Parse the request → Find relevant benchmark datasets → Fetch top models from leaderboards → …
  • Model selection questions
  • SKILL.md covers Step 1: Parse the request, Step 2: Find relevant…, Step 3: Fetch top models from… and Step 4: Enrich with model…, plus 3 more sections
  • Calls jq, curl and hf; reaches huggingface.co

What it does

Huggingface Best is an agent skill from waybarrios/opencode-power-pack. Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Hugging Face and llama.cpp. The repository describes itself as: 54 rigorous skills for Codex, OpenCode, and Pi: code review, security audit, feature development, frontend design, MCP tools, Hugging Face ML/training, and more. The licence is Apache-2.0.

When your agent uses it

  • Model selection questions
  • Use huggingface-local-models for GGUF setup and hf-cli for Hub operations

Example prompts

  • “/huggingface-best”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Parse the request
  2. Find relevant benchmark datasets
  3. Fetch top models from leaderboards
  4. Enrich with model metadata
  5. Filter and rank
  6. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 9dccb6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq
    • curl
    • hf

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • huggingface.co

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Huggingface Best loads about 1.4k tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 594 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from waybarrios/opencode-power-pack at commit 9dccb6d, republished under its Apache-2.0 licence (© waybarrios). 594 words, ~1,371 tokens.

Download SKILL.mdSave it as .claude/skills/huggingface-best/SKILL.md (or your agent's skills folder).
name
huggingface-best
description
Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.
license
Apache-2.0 (modified; see UPSTREAMS.json)

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. All subsequent calls in this skill reuse the same auth token, exported once:

bash
export HF_AUTH="Bearer $(cat ~/.cache/huggingface/token)"
curl -s -H "Authorization: $HF_AUTH" \
  "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:

bash
curl -s -H "Authorization: $HF_AUTH" \
  "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.

bash
# REST API
curl -s -H "Authorization: $HF_AUTH" \
  "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.)
Show full SKILL.md (264 more words)Show less

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 |
|---|-------|--------|--------------|--------------|---------|-----------|
| ⭐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

© waybarrios, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/huggingface-best of waybarrios/opencode-power-pack.

Open the folder on GitHubat commit 9dccb6d

Compare with similar skills

Huggingface Best next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Huggingface Best compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Huggingface Best this skillwaybarrios/opencode-power-pack534—~1.4kAutomated safety check: PassApache-2.0
Qwen Mtp GgufR6410418/Jackrong-llm-finetuning-guide1.7k—~1.7kAutomated safety check: PassMIT
Hugging Face LLM Trainerhuggingface/skills11k1 repos~7.2kAutomated safety check: PassApache-2.0
Hugging Face Local Modelshuggingface/skills11k3 repos~945Automated safety check: PassApache-2.0
Add Modelguoqingbao/xinfer334—~4.2kAutomated safety check: NotesMIT
Resolvealexziskind1/model-shelf130—~792Automated safety check: PassMIT

Similar skills

  • Qwen Mtp Gguf

    R6410418/Jackrong-llm-finetuning-guide

    Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.

    1.7k GitHub stars~1.7k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

  • Hugging Face LLM Trainer

    huggingface/skills

    Official

    Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.

    11k GitHub starsUsed in 1 repo~7.2k tokens
    AI & LLM EngineeringAuto-check passed

  • Hugging Face Local Models

    huggingface/skills

    Official

    Finds llama.cpp-compatible GGUF models on the Hugging Face Hub, picks a quantization for your hardware and launches them with llama-cli or llama-server.

    11k GitHub starsUsed in 3 repos~945 tokens
    AI & LLM EngineeringAuto-check passed

  • Add Model

    guoqingbao/xinfer

    Adapt and port new LLM model architectures to this xinfer project.

    334 GitHub stars~4.2k tokensUpdated 1 mo ago
    AI & LLM EngineeringAuto-check: notes

  • Resolve

    alexziskind1/model-shelf

    Always resolve Hugging Face models via model-shelf before any download.

    130 GitHub stars~792 tokensUpdated 1 mo ago
    AI & LLM EngineeringAuto-check passed

  • Xybrid Init

    xybrid-ai/xybrid

    Generate model metadata for an ML model so it works with xybrid.

    469 GitHub stars~3k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed

More from waybarrios/opencode-power-pack

All 32 skills in this repo

  • Hf Cloud Sagemaker Iam Preflight

    waybarrios/opencode-power-pack

    Verify or select a SageMaker execution role before creating models, endpoints, or training jobs.

    534 GitHub stars~1.6k tokensUpdated 5 days ago
    Auto-check passed

  • Huggingface LLM Trainer

    waybarrios/opencode-power-pack

    Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion.

    534 GitHub stars~3k tokensUpdated 5 days ago
    Auto-check passed

  • Huggingface Vision Trainer

    waybarrios/opencode-power-pack

    Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs.

    534 GitHub stars~2.7k tokensUpdated 5 days ago
    Auto-check passed

  • Codeql

    waybarrios/opencode-power-pack

    Run CodeQL database creation and security queries, add data-extension models, or process CodeQL SARIF.

    534 GitHub starsUsed in 2 repos~3.7k tokens
    Auto-check passed

  • Semgrep

    waybarrios/opencode-power-pack

    Run Semgrep static analysis across a codebase, optionally using Semgrep Pro for cross-file taint analysis.

    534 GitHub stars~2.4k tokensUpdated 5 days ago
    Auto-check passed

  • Insecure Defaults

    waybarrios/opencode-power-pack

    Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production.

    534 GitHub starsUsed in 2 repos~1.3k tokens
    Auto-check passed

Works with

Categories

Questions about Huggingface Best

What does Huggingface Best do?

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Huggingface Best is an agent skill from waybarrios/opencode-power-pack. Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints.

When should I use Huggingface Best?

Huggingface Best fits situations like: model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

How do I install Huggingface Best in Claude Code?

Run `npx skills add waybarrios/opencode-power-pack --skill huggingface-best -a claude-code`. Or copy the skill folder (skills/huggingface-best in waybarrios/opencode-power-pack) into .claude/skills/huggingface-best in your project. Claude Code loads it when a task matches its description.

How do I install Huggingface Best in Codex?

Run `npx skills add waybarrios/opencode-power-pack --skill huggingface-best -a codex`. Or copy the skill folder (skills/huggingface-best in waybarrios/opencode-power-pack) into .agents/skills/huggingface-best in your project. Codex loads it when a task matches its description.

Can I use Huggingface Best in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add waybarrios/opencode-power-pack --skill huggingface-best -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/huggingface-best, .gemini/skills/huggingface-best, .github/skills/huggingface-best and .opencode/skills/huggingface-best in your project.

What does Huggingface Best need to run?

Going by SKILL.md and its folder, Huggingface Best needs the command-line tools its instructions call (jq, curl and hf).

Does Huggingface Best access the network?

SKILL.md names 1 domain. In commands or code: huggingface.co; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Huggingface Best safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Huggingface Best use?

Huggingface Best is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Huggingface Best use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Huggingface Best?

Skills that share tags, products or a category with Huggingface Best: Qwen Mtp Gguf (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), Hugging Face Local Models (huggingface/skills, 11k stars) and Add Model (guoqingbao/xinfer, 334 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Huggingface Best?

waybarrios (a GitHub user) maintains it in waybarrios/opencode-power-pack, which has 534 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 6, 2026.

Source: waybarrios/opencode-power-pack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.