SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
A skill your agent uses when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks…
Install Hugging Face CLI Workflows
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflows --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .claude/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.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/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .claude/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflowsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflows --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .agents/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .agents/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflows --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .cursor/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .cursor/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/burtenshaw/training-agents.git --path .agents/skills/hugging-face-cli-workflows--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflows --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .gemini/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .gemini/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflowsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .github/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .github/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install burtenshaw/training-agents hugging-face-cli-workflows --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/burtenshaw/training-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/hugging-face-cli-workflows .opencode/skills/hugging-face-cli-workflows && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-cli-workflows" agent skill from https://github.com/burtenshaw/training-agents/tree/main/.agents/skills/hugging-face-cli-workflows into .opencode/skills/hugging-face-cli-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli-workflows", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
hugging-face-cli-workflows- GitHub stars
- 153
- Token cost
- ~298 tokens
- SKILL.md length
- 126 words
- Files
- 4 (incl. references)
- Skills in repo
- 6
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks…
- Works in 5 steps: Check auth with hf auth whoami before… → Identify the target type: model repo,… → Keep scripts portable: remote Jobs… → …
- Working with Hugging Face CLI
- SKILL.md covers Workflow, Safety and References
- Calls hf; needs HF_TOKEN
What it does
Hugging Face CLI Workflows is an agent skill from burtenshaw/training-agents. Use when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks, Space links, and remote artifact movement.
Its SKILL.md is about 300 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/hub-workflows.md` and `references/jobs-workflows.md`).
It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Hugging Face. The repository describes itself as: A repo on resources for training agents. The licence is Apache-2.0.
When your agent uses it
- Working with Hugging Face CLI
- Hub workflows for TRL training
- Model persistence
- Remote artifact movement
Example prompts
- “/hugging-face-cli-workflows”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Check auth with hf auth whoami before remote actions.
- Identify the target type: model repo, dataset repo, Space, Job, or bucket.
- Keep scripts portable: remote Jobs cannot read local files unless the script
- Ensure ephemeral runners push or upload artifacts before completion.
- Summarize exact repo ids, job ids, paths, and commands.
What it can do on your machine
Read from SKILL.md and the folder at commit ec7cc54. 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:
hf
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
HF_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Hugging Face CLI Workflows loads about 298 tokens when it runs, and up to ~611 if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 126 words of instructions outside code blocks.
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 burtenshaw/training-agents at commit ec7cc54, republished under its Apache-2.0 licence (© burtenshaw). 126 words, ~298 tokens.
Download SKILL.mdSave it as
.claude/skills/hugging-face-cli-workflows/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- hugging-face-cli-workflows
- description
- Use when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks, Space links, and remote artifact movement.
Hugging Face CLI Workflows
Use this skill for Hub, CLI, Jobs, buckets, and artifact workflows around TRL training.
Workflow
- Check auth with
hf auth whoamibefore remote actions. - Identify the target type: model repo, dataset repo, Space, Job, or bucket.
- Keep scripts portable: remote Jobs cannot read local files unless the script is uploaded, inlined, or available by URL.
- Ensure ephemeral runners push or upload artifacts before completion.
- Summarize exact repo ids, job ids, paths, and commands.
Safety
- Never print
HF_TOKENor credentials. - Use private repos or buckets for non-public data by default.
- Confirm before deleting or overwriting Hub artifacts.
- Keep large checkpoints outside this context repository.
References
references/hub-workflows.md: auth, repos, upload/download, and artifacts.references/jobs-workflows.md: HF Jobs conventions for TRL runs.
© burtenshaw, 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
SKILL.md and 3 other files (references) in .agents/skills/hugging-face-cli-workflows of burtenshaw/training-agents.
- SKILL.md
- agents/openai.yaml
- references/hub-workflows.md
- references/jobs-workflows.md
Open the folder on GitHubat commit ec7cc54
Compare with similar skills
Hugging Face CLI Workflows 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hugging Face CLI Workflows this skillburtenshaw/training-agents | 153 | — | ~298 | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face LLM Trainerhuggingface/skills | 11k | 3 repos | ~7.2k | Automated safety check: Pass | Apache-2.0 | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Upload Post Imagehuggingface/blog | 3.5k | — | ~1.1k | Automated safety check: Pass | None | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 |
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Questions about Hugging Face CLI Workflows
What does Hugging Face CLI Workflows do?
A skill your agent uses when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks…. Hugging Face CLI Workflows is an agent skill from burtenshaw/training-agents. Use when working with Hugging Face CLI or Hub workflows for TRL training, including auth, repositories, uploads, downloads, Jobs, buckets, model persistence, dataset checks, Space links, and remote artifact movement.
When should I use Hugging Face CLI Workflows?
Hugging Face CLI Workflows fits situations like: working with Hugging Face CLI; hub workflows for TRL training; model persistence; remote artifact movement.
How do I install Hugging Face CLI Workflows in Claude Code?
Run `npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a claude-code`. Or copy the skill folder (.agents/skills/hugging-face-cli-workflows in burtenshaw/training-agents) into .claude/skills/hugging-face-cli-workflows in your project. Claude Code loads it when a task matches its description.
How do I install Hugging Face CLI Workflows in Codex?
Run `npx skills add burtenshaw/training-agents --skill hugging-face-cli-workflows -a codex`. Or copy the skill folder (.agents/skills/hugging-face-cli-workflows in burtenshaw/training-agents) into .agents/skills/hugging-face-cli-workflows in your project. Codex loads it when a task matches its description.
Can I use Hugging Face CLI Workflows 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 burtenshaw/training-agents --skill hugging-face-cli-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-cli-workflows, .gemini/skills/hugging-face-cli-workflows, .github/skills/hugging-face-cli-workflows and .opencode/skills/hugging-face-cli-workflows in your project.
What does Hugging Face CLI Workflows need to run?
Going by SKILL.md and its folder, Hugging Face CLI Workflows needs the command-line tools its instructions call (hf) and credentials named HF_TOKEN.
Does Hugging Face CLI Workflows access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Hugging Face CLI Workflows 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 Hugging Face CLI Workflows use?
Hugging Face CLI Workflows is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Hugging Face CLI Workflows use?
About 298 tokens (SKILL.md is roughly 1.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 313 tokens, read only when the agent opens those files.
What are the alternatives to Hugging Face CLI Workflows?
Skills that share tags, products or a category with Hugging Face CLI Workflows: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Upload Post Image (huggingface/blog, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Hugging Face CLI Workflows?
burtenshaw (a GitHub user) maintains it in burtenshaw/training-agents, which has 153 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on September 13, 2026.
Source: burtenshaw/training-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.