Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool. Model and Dataset cards can be accessed from repositories directly.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionhugging-face-tool-builderExecute the skills CLI command in your project's root directory to begin installation:
Fetches hugging-face-tool-builder from huggingface/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate hugging-face-tool-builder. Access via /hugging-face-tool-builder in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
10.1K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
10.1K
stars
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool. Model and Dataset cards can be accessed from repositories directly.
Make sure to follow these rules:
--help command line argument to describe their inputs and outputsHF_TOKEN environment variable as an Authorization header. For example: curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/. This provides higher rate limits and appropriate authorization for data access.Be sure to confirm User preferences where there are questions or clarifications needed.
Paths below are relative to this skill directory.
Reference examples:
references/hf_model_papers_auth.sh — uses HF_TOKEN automatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated/private content.references/find_models_by_paper.sh — optional HF_TOKEN usage via --token, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.references/hf_model_card_frontmatter.sh — uses the hf CLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.Baseline examples (ultra-simple, minimal logic, raw JSON output with HF_TOKEN header):
references/baseline_hf_api.sh — bashreferences/baseline_hf_api.py — pythonreferences/baseline_hf_api.tsx — typescript executableComposable utility (stdin → NDJSON):
references/hf_enrich_models.sh — reads model IDs from stdin, fetches metadata per ID, emits one JSON object per line for streaming pipelines.Composability through piping (shell-friendly JSON output):
references/baseline_hf_api.sh 25 | jq -r '.[].id' | references/hf_enrich_models.sh | jq -s 'sort_by(.downloads) | reverse | .[:10]'references/baseline_hf_api.sh 50 | jq '[.[] | {id, downloads}] | sort_by(.downloads) | reverse | .[:10]'printf '%s\n' openai/gpt-oss-120b meta-llama/Meta-Llama-3.1-8B | references/hf_model_card_frontmatter.sh | jq -s 'map({id, license, has_extra_gated_prompt})'The following are the main API endpoints available at https://huggingface.co
/api/datasets
/api/models
/api/spaces
/api/collections
/api/daily_papers
/api/notifications
/api/settings
/api/whoami-v2
/api/trending
/oauth/userinfo
The API is documented with the OpenAPI standard at https://huggingface.co/.well-known/openapi.json.
IMPORTANT: DO NOT ATTEMPT to read https://huggingface.co/.well-known/openapi.json directly as it is too large to process.
IMPORTANT Use jq to query and extract relevant parts. For example,
Command to Get All 160 Endpoints
curl -s "https://huggingface.co/.well-known/openapi.json" | jq '.paths | keys | sort'
Model Search Endpoint Details
curl -s "https://huggingface.co/.well-known/openapi.json" | jq '.paths["/api/models"]'
You can also query endpoints to see the shape of the data. When doing so constrain results to low numbers to make them easy to process, yet representative.
The hf command line tool gives you further access to Hugging Face repository content and infrastructure.
❯ hf --help
Usage: hf [OPTIONS] COMMAND [ARGS]...
Hugging Face Hub CLI
Options:
--help Show this message and exit.
Commands:
auth Manage authentication (login, logout, etc.).
cache Manage local cache directory.
download Download files from the Hub.
endpoints Manage Hugging Face Inference Endpoints.
env Print information about the environment.
jobs Run and manage Jobs on the Hub.
repo Manage repos on the Hub.
repo-files Manage files in a repo on the Hub.
upload Upload a file or a folder to the Hub.
upload-large-folder Upload a large folder to the Hub.
version Print information about the hf version.
The hf CLI command has replaced the now deprecated huggingface_hub CLI command.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
anthropics/claude-code
mblode/agent-skills
github/awesome-copilot
leonxlnx/taste-skill
sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
We added hugging-face-tool-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: hugging-face-tool-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
hugging-face-tool-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added hugging-face-tool-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: hugging-face-tool-builder is focused, and the summary matches what you get after install.
hugging-face-tool-builder reduced setup friction for our internal harness; good balance of opinion and flexibility.
hugging-face-tool-builder reduced setup friction for our internal harness; good balance of opinion and flexibility.
hugging-face-tool-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
hugging-face-tool-builder reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend hugging-face-tool-builder for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 48