LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Verify your Hugging Face account and search the model hub. An agent skill from Anil-matcha/awesome-muse-connectors.
Install Huggingface
$ npx skills add Anil-matcha/awesome-muse-connectors --skill huggingface -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors huggingface --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/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .claude/skills && cp -r skills-src/connectors/huggingface .claude/skills/huggingface && 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 "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .claude/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingfaceType 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 Anil-matcha/awesome-muse-connectors --skill huggingface -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors huggingface --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .agents/skills && cp -r skills-src/connectors/huggingface .agents/skills/huggingface && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .agents/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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 Anil-matcha/awesome-muse-connectors --skill huggingface -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors huggingface --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/connectors/huggingface .cursor/skills/huggingface && 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 "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .cursor/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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/Anil-matcha/awesome-muse-connectors.git --path connectors/huggingface--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 Anil-matcha/awesome-muse-connectors --skill huggingface -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors huggingface --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/connectors/huggingface .gemini/skills/huggingface && 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 "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .gemini/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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 Anil-matcha/awesome-muse-connectors huggingfaceInstalls 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 Anil-matcha/awesome-muse-connectors --skill huggingface -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .github/skills && cp -r skills-src/connectors/huggingface .github/skills/huggingface && 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 "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .github/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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 Anil-matcha/awesome-muse-connectors --skill huggingface -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors huggingface --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/connectors/huggingface .opencode/skills/huggingface && 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 "huggingface" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/huggingface into .opencode/skills/huggingface/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface", 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
huggingface- GitHub stars
- 1.3k
- Token cost
- ~431 tokens
- SKILL.md length
- 139 words
- Files
- 2
- Skills in repo
- 153
- Repo updated
- First seen
- Licence
- MIT
At a glance
Verify your Hugging Face account and search the model hub. An agent skill from Anil-matcha/awesome-muse-connectors.
- Works in 2 steps: This skill is read-only. No repository… → Never exfiltrate the credential: the CLI…
- Phrases: hugging face
- SKILL.md covers Purpose, Tooling, Auth and Operating Rules, plus 2 more sections
- Runs Python scripts from its folder
What it does
Huggingface is an agent skill from Anil-matcha/awesome-muse-connectors. Verify your Hugging Face account and search the model hub. Read-only. Trigger phrases: hugging face, huggingface, hf model, search models.
Its SKILL.md is about 430 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `bin/huggingface.py`).
It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Hugging Face. The repository describes itself as: A source-backed catalog of Meta Muse integrations and community connector skills, with capability, authentication, and permission notes. The licence is MIT.
When your agent uses it
- Phrases: hugging face
- Tasks that involve Model hubs and datasets
Example prompts
- “/huggingface”
Requirements
- Python 3
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- This skill is read-only. No repository creation, upload, or delete commands ship.
- Never exfiltrate the credential: the CLI only ever handles surrogates (see bin/huggingface.py). Do not print, log, or transmit the token…
What it can do on your machine
Read from SKILL.md and the folder at commit d6dc5d8. 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
Ships script files (Python), which the agent can run.
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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Huggingface loads about 431 tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 139 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 Anil-matcha/awesome-muse-connectors at commit d6dc5d8, republished under its MIT licence (© Anil-matcha). 139 words, ~431 tokens.
Download SKILL.mdSave it as
.claude/skills/huggingface/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- huggingface
- description
- Verify your Hugging Face account and search the model hub. Read-only. Trigger phrases: hugging face, huggingface, hf model, search models.
- metadata.includeInPrompt
- true
- tagline
- Verify your Hugging Face account and search the model hub. Read-only.
- catalog_auth
- Hugging Face user access token (per-user, huggingface.co/settings/tokens)
- catalog_hosts
- huggingface.co
Hugging Face
Purpose
Verify the user's Hugging Face account and search the public model hub: whoami (name, email), and model search by keyword with likes/downloads. Read-only: no repo writes ship in this skill.
Tooling
All commands go through bin/huggingface.py:
bin/huggingface.py me # whoami: name, email, account type
bin/huggingface.py models --query llama # search the model hub (id, likes, downloads), 10 resultsAuth
- Provider id:
huggingface(credential is collected ascustom.huggingface) - Collection: user access token via the secure credential flow (
credentials.request_api_access): create one at huggingface.co/settings/tokens (a fine-grained read token is enough) - Connect placement:
bearer_header - Allowed hosts:
huggingface.co - Status check:
bin/huggingface.py me(a successful whoami proves the token works)
Operating Rules
- This skill is read-only. No repository creation, upload, or delete commands ship.
- Never exfiltrate the credential: the CLI only ever handles surrogates (see
bin/huggingface.py). Do not print, log, or transmit the token value.
Files
- SKILL.md
- bin/huggingface.py
Maturity
🧪 Draft: written from Hugging Face's public Hub API docs; not yet live-tested end-to-end.
© Anil-matcha, MIT. 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 1 other file in connectors/huggingface of Anil-matcha/awesome-muse-connectors.
- SKILL.md
- bin/huggingface.py
Open the folder on GitHubat commit d6dc5d8
Compare with similar skills
Huggingface 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 |
|---|---|---|---|---|---|---|
| Huggingface this skillAnil-matcha/awesome-muse-connectors | 1.3k | — | ~431 | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Upload Post Imagehuggingface/blog | 3.5k | — | ~1.1k | Automated safety check: Pass | None | |
| Add Archon Modelareal-project/AReaL | 5.8k | — | ~4.9k | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
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.
- Official
Hugging Face Local Model Evals
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
- Official
Upload Post Image
huggingface/blog
A skill your agent uses when adding or migrating non-thumbnail images for a Hugging Face Blog post.
Add Archon Model
areal-project/AReaL
Guide for adding a new model to the Archon engine. An agent skill from areal-project/AReaL.
Esmfold2
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
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Questions about Huggingface
What does Huggingface do?
Verify your Hugging Face account and search the model hub. An agent skill from Anil-matcha/awesome-muse-connectors. Huggingface is an agent skill from Anil-matcha/awesome-muse-connectors. Verify your Hugging Face account and search the model hub.
When should I use Huggingface?
Huggingface fits situations like: phrases: hugging face; tasks that involve Model hubs and datasets.
How do I install Huggingface in Claude Code?
Run `npx skills add Anil-matcha/awesome-muse-connectors --skill huggingface -a claude-code`. Or copy the skill folder (connectors/huggingface in Anil-matcha/awesome-muse-connectors) into .claude/skills/huggingface in your project. Claude Code loads it when a task matches its description.
How do I install Huggingface in Codex?
Run `npx skills add Anil-matcha/awesome-muse-connectors --skill huggingface -a codex`. Or copy the skill folder (connectors/huggingface in Anil-matcha/awesome-muse-connectors) into .agents/skills/huggingface in your project. Codex loads it when a task matches its description.
Can I use Huggingface 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 Anil-matcha/awesome-muse-connectors --skill huggingface -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, .gemini/skills/huggingface, .github/skills/huggingface and .opencode/skills/huggingface in your project.
What does Huggingface need to run?
Going by SKILL.md and its folder, Huggingface needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Huggingface 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 Huggingface 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 use?
Huggingface is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Huggingface use?
About 431 tokens (SKILL.md is roughly 1.7k 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?
Skills that share tags, products or a category with Huggingface: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k 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 Huggingface?
Anil-matcha (a GitHub user) maintains it in Anil-matcha/awesome-muse-connectors, which has 1,346 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.
Source: Anil-matcha/awesome-muse-connectors on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.