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Download primitives for HuggingFace assets - files, folder snapshots, and model weights with exponential backoff on rate limits. Use when pulling models, datasets, or caches from HuggingFace to the local environment.
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
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npx skills add https://github.com/richfrem/agent-plugins-skills --skill hf-downloadコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Scaffolds a new top-level agent plugin directory. NOT for scaffolding single skills (use `create-skill`) and NOT for adding MCP integrations to existing plugins (use `create-mcp-integration`).
This skill should be used when the user wants to "create a marketplace", "setup a marketplace catalog", "scaffold marketplace.json", "initialize a plugin registry", or "configure a Gemini CLI extension". Use this even if they just mention "setting up a marketplace".
Cooperative Multi-Agent Coordination Loop. Spawns a lightweight companion sub-agent (Gemini 3.5 Flash Low) to perform spec discovery, planning, and implementation. The primary agent (Claude) acts as the QA Director, answering Gemini's questions, approving the spec/plan, and running verification tests. Assumes the superpowers plugin is installed in the target repository.
SOC 職業分類に基づく
SKILL.md を表示中
| name | hf-download |
| plugin | huggingface-utils |
| description | Download primitives for HuggingFace assets - files, folder snapshots, and model weights with exponential backoff on rate limits. Use when pulling models, datasets, or caches from HuggingFace to the local environment. |
| allowed-tools | Bash, Read |
This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
Status: Active
Author: Richard Fremmerlid
Domain: HuggingFace Integration
Depends on: hf-init (credentials must be configured first)
Provides consolidated download operations for all HF-consuming plugins (Primary Agent, local-llm-bench, etc.) to fetch files, models, and snapshots. All downloads include exponential backoff for rate-limit handling.
| Function | Description | Source Repo |
|---|---|---|
download_file() | Download a single file | Custom or default repo |
download_folder() | Download an entire folder snapshot | Custom or default repo |
from hf_download import download_file, download_folder
from pathlib import Path
# Download a single file from dataset repository to local directory
local_file_path = await download_file(
filename="data/soul_traces.jsonl",
local_dir=Path("./local_data")
)
# Download a model snapshot (e.g. GGUF weights)
model_dir = await download_folder(
local_dir=Path("./models"),
repo_id="unsloth/gemma-4-12b-it-GGUF",
repo_type="model",
allow_patterns=["*UD-Q4_K_XL.gguf"]
)
# Download a single file
python ./hf_download.py --filename data/soul_traces.jsonl --local-dir ./local_data
# Download a specific model snapshot using glob patterns
python ./hf_download.py \
--repo-id unsloth/gemma-4-12b-it-GGUF \
--repo-type model \
--allow-patterns "*UD-Q4_K_XL.gguf" \
--local-dir ./models
hf-init first to validate credentials and dataset structure.huggingface_hub installed (pip install huggingface_hub).HUGGING_FACE_USERNAME, HUGGING_FACE_TOKEN.All operations return paths on success or raise appropriate exceptions with exponential backoff retries (up to 5 attempts) on rate limits or API connectivity issues.