hf-cloud-serving-image-selection agent skill by huggingface

At a glance

Before you install

Read from the skill's files at the directory's October 19, 2026 snapshot: what ships with it, what its code reads, and the terms it is published under.

• Bundled scripts: 1 script file (1 Python). Your agent may run them, so read them first.
• Credential names SKILL.md mentions: HUGGING_FACE_HUB_TOKEN, HF_TOKEN.
• License: Apache-2.0, an open-source or open-content license.

Install hf-cloud-serving-image-selection

The skills CLI works with Claude Code, Codex, Cursor and other agents; the other routes copy the folder by hand.

npx skills add https://github.com/huggingface/skills/tree/main/skills/hf-cloud-serving-image-selection

tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/huggingface/skills.git "$tmp/repo"
mkdir -p .claude/skills
cp -R "$tmp/repo/"skills/hf-cloud-serving-image-selection .claude/skills/
rm -rf "$tmp"

python3 ~/.codex/skills/.system/skill-installer/scripts/install-skill-from-github.py \
    --repo huggingface/skills \
    --path skills/hf-cloud-serving-image-selection

huggingface/skills is third-party code. Read SKILL.md and any bundled scripts before you install it.

How the author describes it

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

From the description field of SKILL.md. Agents read this line to decide when to load the skill.

Sections in SKILL.md

• Rule zero: HuggingFace images always win
• Where image URIs come from
• Quick decision
• Rerankers: TEI or vLLM?
• Workflow
• TEI: pick the right variant
• vLLM AMI requirement
• Configuring the vLLM DLCs (HuggingFace vLLM and AWS vLLM)
• Required for every HuggingFace LLM deployment
• Tuning (optional)
• Configuring TEI
• CUDA / instance compatibility
• VPC / NAT gateway problem
• When the catalog page won't render, is stale, or is wrong
• Known-broken images (last checked July 2026)
• Hub download time at first boot

Files in the skill folder

• SKILL.md
• references/model-to-image.md
• scripts/mirror_image.py

Suede Skill Check: 84/100 · B

hf-cloud-serving-image-selection passes the required frontmatter check and all seven graded checks, which cover the SKILL.md format, a declared license and a commit in the last year. Those checks earn 56 of the score’s points; the rest come from description quality, instruction depth, bundled files, maintenance and curated-list citations. How the score works.

Where this skill is cited

huggingface/skills appears in 2 curated lists: heilcheng/awesome-agent-skills, skillmatic-ai/awesome-agent-skills.

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