Cloudflare Skills vs Hugging Face Skills: both are coding tools on Scriptiii AI Tools. Cloudflare Skills pricing: Free. Free to use. (reported by the maker) Hugging Face Skills pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
| Field | Cloudflare Skills | Hugging Face Skills |
|---|---|---|
| Page | Cloudflare Skills | Hugging Face Skills |
| Pricing (maker-reported) | Free. Free to use. | Free. Free to use. |
| Upvotes | 0 | 0 |
| Best for | ai-agents, agent-skill, skill-pack | ai-agents, agent-skill, skill-pack |
| Tagline | Cloudflare skills that teach agents how to build on Workers, Durable Objects and other Cloudflare products. | Skills for agents to work with Hugging Face Hub, datasets, model training and evaluation. |
| Website | Visit Cloudflare Skills | Visit Hugging Face Skills |
Pricing and pricing details come from the maker's submission. They are not independently verified.
A collection of Agent Skills for building on Cloudflare, Workers, the Agents SDK, and the wider Cloudflare Developer Platform.
Use the native plugin where supported to install both Cloudflare guidance and the Cloudflare MCP server. Agents that only support the Agent Skills standard can install the skills separately.
Install from the Cloudflare plugin marketplace:
codex plugin marketplace add cloudflare/skills
codex plugin add cloudflare@cloudflare
Start a new Codex session after installation.
Install using the plugin marketplace:
/plugin marketplace add cloudflare/skills
/plugin install cloudflare@cloudflare
Install directly from this repository:
chat.plugins.enabled in VS Code settings.https://github.com/cloudflare/skills.For marketplace installation or troubleshooting, see VS Code's agent plugin documentation.
Install from the Cursor Marketplace or add manually via Settings > Rules > Add Rule > Remote Rule (Github) with cloudflare/skills.
Install using the npx skills CLI:
npx skills add https://github.com/cloudflare/skills
Clone this repo and copy the skill folders into the appropriate directory for your agent:
| Agent | Skill Directory | Docs |
|---|---|---|
| Claude Code | ~/.claude/skills/ | docs |
| Cursor | ~/.cursor/skills/ | docs |
| OpenCode | ~/.config/opencode/skills/ | docs |
| OpenAI Codex | ~/.codex/skills/ | docs |
| Pi | ~/.pi/agent/skills/ | docs |
Skills are contextual and auto-loaded based on your conversation. When a request matches a skill's triggers, the agent loads and applies the relevant skill to provide accurate, up-to-date guidance.
| Skill | Useful for |
|---|---|
| cloudflare | Helps agents discover which Cloudflare products fit their needs, then find the right skills and docs |
| basin | Building analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL; includes the Data Platform rebrand |
| k2 | Building durable K2 Streams with HTTP or Worker producers and subscription consumers |
| frameworks-on-cloudflare | Astro, SvelteKit, React Router, TanStack Start, Nuxt, Hono, and SPAs on Workers; routes to each framework's Workers guide and agent guidance |
| nextjs-on-cloudflare | Next.js on Workers with vinext; routes to vinext's upstream skills and docs |
| agents-sdk | Building stateful AI agents with state, scheduling, RPC, MCP servers, email, and streaming chat |
| durable-objects | Stateful coordination (chat rooms, games, booking), RPC, SQLite, alarms, WebSockets |
| sandbox-next | Sandbox on @cloudflare/sandbox@next (1.0 preview); recommended for new projects |
| sandbox-stable | Sandbox on the current stable @cloudflare/sandbox package |
| sandbox-migrate-to-next | Port a stable Sandbox app to @cloudflare/sandbox@next |
| cf-cli | Finding and running Cloudflare CLI (cf) commands, and choosing between cf and Wrangler |
| wrangler | Deploying and managing Workers, KV, R2, D1, Vectorize, Queues, Workflows |
| cloudflare-app-bootstrap | Getting a new or checked-out app to a working local request: bindings, secrets, initial data, and verification |
| workers-best-practices | Writing, reviewing, or configuring production Workers |
| workers-development | Local development and debugging with Local Explorer: binding data, traces, and logs; routes deployed-Worker debugging to observability |
| workers-profiling | Profiling CPU usage and allocation hotspots in deployed Workers and Durable Objects |
| cloudflare-email-service | Implementing or troubleshooting Email Sending, Email Routing, and delivery configuration |
This plugin includes Cloudflare's main remote MCP server for enhanced functionality:
| Server | Purpose |
|---|---|
| cloudflare | Access the Cloudflare API and current developer documentation through the Code Mode MCP server |
Cloudflare also publishes product-specific MCP servers. This plugin intentionally bundles only the main cloudflare server.
Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation. The client plugin marketplaces expose the hf-cli skill as the bootstrap path for core Hub operations; additional workflow skills can be installed on demand with hf skills add <skill-name> or discovered by skill-aware clients over CLI/MCP integrations.
The skills in this repository follow the standardized Agent Skills format.
[!NOTE]
Just want to give your agent access to the Hugging Face Hub? Start with . It's the recommended first Skill to install: it teaches your agent every command (search models, manage datasets and buckets, launch Spaces, run jobs) and is generated from your locally installed CLI so it stays current.
| turnstile-spin | Setting up, repairing, or migrating Turnstile bot verification, including server-side Siteverify |
| web-perf | Auditing Core Web Vitals (FCP, LCP, TBT, CLS), render-blocking resources, network chains |
| cloudflare-one | Designing, configuring, troubleshooting, or reviewing Cloudflare One deployments across Access, Gateway, WARP, Tunnel, Magic WAN, DLP, CASB, posture, and identity, including service-token access for agents and other non-browser clients |
| cloudflare-one-migrations | Migration assessments, policy mapping, rollout plans, and gap analysis for Zscaler, Palo Alto, legacy VPN/SWG, and SASE migrations to Cloudflare One |
| gateway-workers | Writing and reviewing Workers invoked by the Forward action in Gateway HTTP policies |
hf-clihfIn practice, skills are self-contained folders that package instructions, scripts, and resources together for an AI agent to use on a specific use case. Each folder includes a SKILL.md file with YAML frontmatter (name and description) followed by the guidance your coding agent follows while the skill is active.
[!TIP]
If your agent doesn't support skills, you can useagentsmd/AGENTS.mddirectly as a fallback.
The hf-cli skill in this repository is also available through:
Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor.
/plugin marketplace add huggingface/skills
/plugin install hf-cli@huggingface/skills
hf CLI:hf skills add <skill-name>
Copy or symlink any skills you want to use from this repository's skills/ directory into one of Codex's standard .agents/skills locations (for example, $REPO_ROOT/.agents/skills or $HOME/.agents/skills) as described in the Codex Skills guide.
Once a skill is available in one of those locations, Codex will discover it using the Agent Skills standard and load the SKILL.md instructions when it decides to use that skill or when you explicitly invoke it.
If your Codex setup still relies on AGENTS.md, you can use the generated agentsmd/AGENTS.md file in this repo as a fallback bundle of instructions.
This repo includes gemini-extension.json to integrate with the Gemini CLI.
Install locally:
gemini extensions install . --consent
or use the GitHub URL:
gemini extensions install https://github.com/huggingface/skills.git --consent
This repository includes Cursor plugin manifests:
.cursor-plugin/plugin.json.mcp.json (configured with the Hugging Face MCP server URL)Install from repository URL (or local checkout) via the Cursor plugin flow. The marketplace entry is intentionally limited to hf-cli; use hf skills add <skill-name> to install additional workflow skills.
For contributors, regenerate manifests with:
./scripts/publish.sh
This repository contains a few skills to get you started. You can also contribute your own skills to the repository.
| Name | Description | Documentation |
|---|---|---|
hf-cli | Hugging Face Hub CLI (hf) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. | SKILL.md |
hf-cloud-aws-context-discovery | Discover the user''s local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. | SKILL.md |
hf-cloud-python-env-setup | Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. | SKILL.md |
hf-cloud-sagemaker-deployment-planner | Plan and coordinate the deployment of a model to Amazon SageMaker AI. | SKILL.md |
hf-cloud-sagemaker-iam-preflight | Ensure a usable SageMaker execution role exists before deploying or training. | SKILL.md |
hf-cloud-sagemaker-production-defaults | Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. | SKILL.md |
hf-cloud-serving-image-selection | Pick the right serving container for a SageMaker model deployment and find its current image URI. | SKILL.md |
hf-mem | Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub | SKILL.md |
huggingface-best | Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. | SKILL.md |
huggingface-community-evals | Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. | SKILL.md |
huggingface-datasets | Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics. | SKILL.md |
huggingface-gradio | Build Gradio web UIs and demos in Python. | SKILL.md |
huggingface-llm-trainer | Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. | SKILL.md |
huggingface-local-models | Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. | SKILL.md |
huggingface-lora-space-builder | Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. | SKILL.md |
huggingface-paper-publisher | Publish and manage research papers on Hugging Face Hub. | SKILL.md |
huggingface-papers | Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. | SKILL.md |
huggingface-spaces | Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. | SKILL.md |
huggingface-tool-builder | Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. | SKILL.md |
huggingface-trackio | Track and visualize ML training experiments with Trackio. | SKILL.md |
huggingface-vision-trainer | Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. | SKILL.md |
huggingface-zerogpu | AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. | SKILL.md |
train-sentence-transformers | Train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker; pair scoring for two-stage retrieval / pair classification), and SparseEncoder (SPLADE, sparse embedding model; for learned-sparse retrieval). | SKILL.md |
transformers-js | Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. | SKILL.md |
trl-training | Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). | SKILL.md |
Once a skill is installed, mention it directly while giving your coding agent instructions:
run_eval_job.py on the latest checkpoint."Your coding agent automatically loads the corresponding SKILL.md instructions and helper scripts while it completes the task.
hf-datasets/) and rename it.SKILL.md frontmatter:
---
name: my-skill-name
description: Describe what the skill does and when to use it
---
# Skill Title
Guidance + examples + guardrails
.claude-plugin/marketplace.json by default. Client marketplaces are intentionally limited to hf-cli; the full Hub CLI marketplace is generated at .claude-plugin/marketplace-internal.json../scripts/publish.sh
The .claude-plugin/marketplace.json and .cursor-plugin/marketplace.json files intentionally expose only hf-cli for client marketplace installation. This keeps install-time manifests focused on core Hub operations and points users to hf skills add <skill-name> for the rest of the repository.
The generated .claude-plugin/marketplace-internal.json file contains the full skill list. Publish automation uploads it to the Hub bucket as marketplace.json so hf skills list, hf skills add, and hf skills update continue to see every available skill.
Newer skill-aware integrations can also pull capabilities dynamically. Hugging Face's discovery flow lets clients search skills, MCP servers, and Spaces, while Skills-over-MCP work is standardizing how skills are discovered and consumed through MCP resources.