Cli And Automation logo

Cli And Automation

Organization
VectorSpaceLab
cli-and-automation

Routes reliable Hugging Face Hub CLI and shell automation, including hf output modes, authentication, file movement, repository and catalog operations, hosted jobs, extensions, and agent-skill generation. Use for hf command selection, JSON or quiet parsing, login and token handling, dry-run or confirmation planning, extensions, or compatible CLI skills.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill namecli-and-automation
Stars
341
Forks
26
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Cli And Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/VectorSpaceLab/AREX-Skill/tree/main/examples/creator/repo-to-skills/artifacts/huggingface_hub/huggingface-hub/sub-skills/cli-and-automation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/examples/creator/repo-to-skills/artifacts/huggingface_hub/huggingface-hub/sub-skills/cli-and-automation .claude/skills/cli-and-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cli And Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cli And Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cli And Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CLI and Automation

Use this sub-skill when a researcher needs to select, explain, or safely automate the hf command-line interface. Treat the installed executable's --help as the release-specific authority: the generated CLI surface changes with the installed huggingface_hub version.

Trigger routes

Load this skill for:

  • “what hf command/flag should I use?” and shell scripts around the Hub;
  • human, JSON, quiet, or agent output, jq/pipeline parsing, or stderr hygiene;
  • login, token selection, HF_TOKEN, auth state, or non-interactive operation;
  • downloads, uploads, hf cp, bucket sync, URI/revision/type selection, or dry-run/plan/apply review;
  • repos, branches, tags, models, datasets, Spaces, cards, search, papers, discussions, webhooks, cache, endpoints, buckets, Jobs, or sandboxes;
  • trusted CLI extension install/update/remove/dispatch or extension authoring;
  • hf skills add, update, preview, marketplace skills, or the implicit skill freshness check.

First route: establish the executable

Read CLI reference before using a detailed flag. Run only safe probes first:

bash
command -v hf && hf --help
hf version
hf --version

hf is the supported entry point. huggingface-cli is a deprecated entry point and may warn and exit instead of providing the old CLI. tiny-agents is a separate MCP/agent CLI, not an alias for hf; inspect it with tiny-agents --help and do not infer its syntax from this skill. If the commands resolve to different installations, report the paths and use the one intended by the current environment.

Safe operating loop

  1. Classify the request as read-only, local-only, Hub mutation, or hosted compute. Ask for the target namespace/repo and permission when a mutation is intended; placeholders are not authorization.
  2. Check hf <group> --help and then hf <group> <command> --help for the installed version. Do not copy a flag from an older generated reference without checking help.
  3. Select an explicit output mode for scripts. Parse stdout only; keep stderr visible for warnings, hints, errors, and progress diagnostics.
  4. Supply credentials through the environment or a supported secret-file mechanism. Never print, echo, log, commit, or put a token in a URL.
  5. For movement or deletion, preview first (--dry-run, --plan, or a read-only list), inspect the exact target and action set, then obtain confirmation before applying.
  6. Pin --repo-type/--revision or encode them in one hf:// URI. Never combine URI-encoded type/revision/path with duplicate positional/options.
  7. Check exit status. For Jobs and Spaces, a streamed log ending is not proof of success; inspect or wait for the terminal status.
  8. Record the command, selected version, output mode, target, and whether the operation was previewed. Redact credentials from logs.

Progressive disclosure

  • Use CLI reference for the command catalog, verified flags, aliases, output semantics, URI rules, safety boundaries, and the skills/extensions surface.
  • Use automation workflows for safe stdout/stderr pipelines, download dry runs, bucket plan/apply, job control, and mutation gates.
  • Use API reference only when output or the lightweight Click-compatible framework explains a surprising CLI behavior.
  • Use development when generated CLI docs, static imports, or release-surface drift is involved.
  • Use troubleshooting for installation, auth, parsing, URI, network, extension, and generated-doc failures.
  • Run the bundled read-only help checker when entry-point health or a bounded help smoke test is useful. It accepts only a trusted executable named hf, bounded timeouts/probe counts, and exact allowlisted built-in command paths with no arguments; every invocation ends in --help. It never invokes a mutating command and always disables startup update checks for its child processes.

Routing map

NeedRoute
Format, parse, auth, shell safetyCLI reference → automation workflows
Download/cache/read pathdownloads-and-storage sibling, then this skill for CLI flags
Upload, repo CRUD, branches/tags, discussionshub-operations sibling, with this skill for command routing
Models/datasets/Spaces catalog or cardsthis skill; route inference endpoint details to its sibling
Jobs, sandboxes, buckets, cloud storagethis skill; use dry-run/plan gates
Python API rather than a terminal commandthe relevant sibling/API skill

This sub-skill does not replace the sibling operating skills for detailed Python APIs, inference behavior, cache internals, or Hub mutation policy. It owns the CLI boundary and automation contract.

Non-negotiable safety rules

  • An example that creates, uploads, deletes, changes visibility, changes a Space, manages a webhook, installs an extension/skill, launches compute, or applies a plan is a mutation. Use a clearly marked mutation example only after target and authorization are confirmed; prefer placeholders.
  • --yes bypasses a confirmation gate; it does not make the target safe. Never add it merely to make a script non-interactive.
  • --format json does not mean every command is non-interactive. In particular, login's browser/menu flow has restrictions; use an environment token for automation.
  • hf auth token intentionally prints a secret. Treat it as a narrow, deliberate pipe, never as a diagnostic command.
  • Do not use hf cp for local-to-local or unsupported bucket-to-repo copies; use the documented alternative. Do not pass a directory to single-file cp.
  • hf skills preview produces generated target-side hf-cli/SKILL.md content. That generated file is distinct from this managed huggingface-hub/sub-skills/cli-and-automation/ skill; do not overwrite or present one as the other.

Verification targets

A useful route ends with observable evidence: help exits zero; stdout parses under the selected mode; stderr remains separate; a dry-run/plan reports the intended actions without payload mutation; refused confirmation exits non-zero; and a final status command verifies the operation. For the difficult synthetic case, use a fake CLI/API fixture or help-only invocation to exercise a hf download --dry-run --format json parse, preserve a warning on stderr, and prove that hf repos delete without --yes is refused before any delete API call. Broad production-marked native tests do not establish this end-to-end shell contract.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cli And Automation AI skill do?

Routes reliable Hugging Face Hub CLI and shell automation, including hf output modes, authentication, file movement, repository and catalog operations, hosted jobs, extensions, and agent-skill generation. Use for hf command selection, JSON or quiet parsing, login and token handling, dry-run or confirmation planning, extensions, or compatible CLI skills.

Why use Cli And Automation on TypingMind?

Because you install it once and use it with any model. Cli And Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cli And Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/examples/creator/repo-to-skills/artifacts/huggingface_hub/huggingface-hub/sub-skills/cli-and-automation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cli And Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cli And Automation?

As many as you like. As long as a model supports skills, you can use Cli And Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cli And Automation AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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