Hugging Face Skills vs Superpowers
In short
Hugging Face Skills vs Superpowers: both are coding tools on Scriptiii AI Tools. Hugging Face Skills pricing: Free. Free to use. (reported by the maker) Superpowers pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
- Hugging Face Skills
- Free. Free to use. (reported by the maker)
- Superpowers
- Free. Free to use. (reported by the maker)
- Upvotes
- 0 vs 0
| Field | Hugging Face Skills | Superpowers |
|---|---|---|
| Page | Hugging Face Skills | Superpowers |
| 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 | Skills for agents to work with Hugging Face Hub, datasets, model training and evaluation. | Skills library and development methodology for coding agents covering planning, TDD and debugging. |
| Website | Visit Hugging Face Skills | Visit Superpowers |
Pricing and pricing details come from the maker's submission. They are not independently verified.
Hugging Face Skills
Hugging Face Skills
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 withhf-cli. It's the recommended first Skill to install: it teaches your agent everyhfcommand (search models, manage datasets and buckets, launch Spaces, run jobs) and is generated from your locally installed CLI so it stays current.
How do Skills work?
In 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:
- Cursor Marketplace (https://cursor.com/marketplace/huggingface)
- Codex Plugins Directory (https://developers.openai.com/codex/plugins)
Installation
Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor.
Claude Code
- Register the repository as a plugin marketplace:
/plugin marketplace add huggingface/skills
- Install the CLI skill:
/plugin install hf-cli@huggingface/skills
- To install another Hugging Face skill, use the
hfCLI:
hf skills add <skill-name>
Codex
-
Copy or symlink any skills you want to use from this repository's
skills/directory into one of Codex's standard.agents/skillslocations (for example,$REPO_ROOT/.agents/skillsor$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.mdinstructions 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 generatedagentsmd/AGENTS.mdfile in this repo as a fallback bundle of instructions.
Gemini CLI
-
This repo includes
gemini-extension.jsonto 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
- See Gemini CLI extensions docs for more help.
Cursor
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
Skills
This repository contains a few skills to get you started. You can also contribute your own skills to the repository.
Available skills
| 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 |
Using skills in your coding agent
Once a skill is installed, mention it directly while giving your coding agent instructions:
- "Use the HF LLM trainer skill to estimate the GPU memory needed for a 70B model run."
- "Use the HF model evaluation skill to launch
run_eval_job.pyon the latest checkpoint." - "Use the HF dataset creator skill to draft new few-shot classification templates."
- "Use the HF paper publisher skill to index my arXiv paper and link it to my model."
Your coding agent automatically loads the corresponding SKILL.md instructions and helper scripts while it completes the task.
Contribute or customize a skill
- Copy one of the existing skill folders (for example,
hf-datasets/) and rename it. - Update the new folder's
SKILL.mdfrontmatter:--- name: my-skill-name description: Describe what the skill does and when to use it --- # Skill Title Guidance + examples + guardrails - Add or edit supporting scripts, templates, and documents referenced by your instructions.
- Do not add the skill to
.claude-plugin/marketplace.jsonby default. Client marketplaces are intentionally limited tohf-cli; the full Hub CLI marketplace is generated at.claude-plugin/marketplace-internal.json. - Run:
to regenerate and validate all generated metadata.
./scripts/publish.sh - Reinstall or reload the skill bundle in your coding agent so the updated folder is available.
Marketplace
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.
Additional references
- Browse the latest instructions, scripts, and templates directly at huggingface/skills.
- Review Hugging Face documentation for the specific libraries or workflows you reference inside each skill.
Superpowers
Superpowers
Superpowers is a complete software development methodology for your coding agents, built on top of a set of composable skills and some initial instructions that make sure your agent uses them.
Table of Contents
- How it works
- Commercial Services
- Getting Started
- The Basic Workflow
- When Something Goes Wrong
- Community
- What's Inside
- Philosophy
- Contributing
- Updating
- License
- Visual companion telemetry
How it works
It starts from the moment you fire up your coding agent. As soon as it sees that you're building something, it doesn't just jump into trying to write code. Instead, it steps back and asks you what you're really trying to do.
Once it's teased a spec out of the conversation, it shows it to you in chunks short enough to actually read and digest.
After you've signed off on the design, your agent puts together an implementation plan that's clear enough for an enthusiastic junior engineer with poor taste, no judgement, no project context, and an aversion to testing to follow. It emphasizes true red/green TDD, YAGNI (You Aren't Gonna Need It), and DRY.
Next up, once you say "go", it launches a subagent-driven-development process, having agents work through each engineering task, inspecting and reviewing their work, and continuing forward. It's not uncommon for your agent to work autonomously for a couple hours at a time without deviating from the plan you put together.
There's a bunch more to it, but that's the core of the system. And because the skills trigger automatically, you don't need to do anything special. Your coding agent just has Superpowers.
Commercial Services
If you're using Superpowers in enterprise and could benefit from commercial support, additional tooling, or managed spending, please don't hesitate to drop us a line at sales@primeradiant.com.
Installation
Installation differs by harness. If you use more than one, install Superpowers separately for each one.
Claude Code
Superpowers is available via the official Claude plugin marketplace
Official Marketplace
-
Install the plugin from Anthropic's official marketplace:
/plugin install superpowers@claude-plugins-official
Superpowers Marketplace
The Superpowers marketplace provides Superpowers and some other related plugins for Claude Code.
-
Register the marketplace:
/plugin marketplace add obra/superpowers-marketplace -
Install the plugin from this marketplace:
/plugin install superpowers@superpowers-marketplace
Antigravity
Install Superpowers as a plugin from this repository:
agy plugin install https://github.com/obra/superpowers
Antigravity lists each installed skill's description, and the description of
the using-superpowers skill prompts the model to load that skill at the start
of a conversation. Reinstall with the same command to update.
Codex App
Superpowers is available via the official Codex plugin marketplace.
- In the Codex app, click on Plugins in the sidebar.
- You should see
Superpowersin the Coding section. - Click the
+next to Superpowers and follow the prompts.
Codex CLI
Superpowers is available via the official Codex plugin marketplace.
-
Open the plugin search interface:
/plugins -
Search for Superpowers:
superpowers -
Select
Install Plugin.
Cursor
-
In Cursor Agent chat, install from marketplace:
/add-plugin superpowers -
Or search for "superpowers" in the plugin marketplace.
Devin CLI
-
Install the plugin from this repository:
devin plugins install obra/superpowers -
Update to the latest version with:
devin plugins update superpowers
Factory Droid
-
Register the marketplace:
droid plugin marketplace add https://github.com/obra/superpowers -
Install the plugin:
droid plugin install superpowers@superpowers
Gemini CLI
-
Install the extension:
gemini extensions install https://github.com/obra/superpowers -
Update later:
gemini extensions update superpowers
GitHub Copilot CLI
-
Register the marketplace:
copilot plugin marketplace add obra/superpowers-marketplace -
Install the plugin:
copilot plugin install superpowers@superpowers-marketplace
Grok Build CLI
Superpowers is available via the official Grok plugin marketplace.
-
Install the plugin from xAI's official marketplace:
grok plugin install superpowers@xai-official --trust -
Or open the marketplace in the TUI, search for Superpowers, and install it:
/marketplace
Kimi Code
Superpowers is available in Kimi Code's plugin marketplace.
-
Open Kimi Code's plugin manager:
/plugins -
Go to
Marketplace>Superpowersand install it. -
Or install directly from this repository:
/plugins install https://github.com/obra/superpowers -
Detailed docs: docs/README.kimi.md
OpenCode
OpenCode uses its own plugin install; install Superpowers separately even if you
already use it in another harness.
-
Tell OpenCode:
Fetch and follow instructions from https://raw.githubusercontent.com/obra/superpowers/refs/heads/main/.opencode/INSTALL.md -
Detailed docs: docs/README.opencode.md
Pi
Install Superpowers as a Pi package from this repository:
pi install git:github.com/obra/superpowers
For local development, run Pi with this checkout loaded as a temporary package:
pi -e /path/to/superpowers
The Pi package loads the Superpowers skills and a small extension that injects the using-superpowers bootstrap at session startup and again after compaction. Pi has native skills, so no compatibility Skill tool is required. Subagent and task-list tools remain optional Pi companion packages.
Qwen Code
Qwen Code installs plugins from Claude Code marketplaces directly.
-
Install the plugin from this repository, and pick
superpowerswhen prompted:qwen extensions install obra/superpowers -
Update later:
qwen extensions update superpowers
Hermes Agent
Install Superpowers as a Hermes plugin from this repository:
hermes plugins install obra/superpowers --enable
Restart any active Hermes sessions after installing. Note: Hermes has no
post-compaction hook, so a very long session that compacts over its first
turn loses the bootstrap — start a fresh session if skills stop triggering.
Muse
Superpowers is available as a native Muse plugin — same repo, same skills, all harnesses. The using-superpowers bootstrap is injected via the native SessionStart hook alongside Claude Code, Codex, Cursor, Gemini, Pi, and the rest — no per-session opt-in.
-
Install from a local checkout:
muse plugins install ./ muse plugins approve superpowersOr clone and install:
git clone https://github.com/obra/superpowers.git muse plugins install ./superpowers muse plugins approve superpowers -
Update later:
muse plugins update superpowers
Restart any active Muse sessions after installing so the SessionStart hook takes effect — skills are active immediately, hooks require approval on first install. To verify, start a fresh session and send Let's make a react todo list — a working install auto-triggers brainstorming before any code is written. Version is tracked in .version-bump.json so scripts/bump-version.sh keeps it in sync.
The Basic Workflow
-
brainstorming - Activates before building anything. Draws out what you actually want, and why, through questions that get you describing it, then plays its understanding back for correction. Small changes get agreed in chat; projects get a written design document a builder could plan from.
-
using-git-worktrees - Activates after design approval. Creates isolated workspace on new branch, runs project setup, verifies clean test baseline.
-
writing-plans - Activates with approved design. Breaks work into bite-sized tasks (2-5 minutes each). Every task has exact file paths, complete code, verification steps.
-
subagent-driven-development or executing-plans - Activates with plan. Either dispatches a fresh subagent per task with a review after each (most thorough), or implements every task inline in the current session with one fresh review of the whole branch at the end (cheapest).
-
test-driven-development - Activates during implementation. Enforces RED-GREEN-REFACTOR: write failing test, watch it fail, write minimal code, watch it pass, commit. Deletes code written before tests.
-
requesting-code-review - Activates between tasks. Reviews against plan, reports issues by severity. Critical issues block progress.
-
finishing-a-development-branch - Activates when tasks complete. Verifies tests, presents options (merge/PR/keep/discard), cleans up worktree.
The agent checks for relevant skills before any task. Mandatory workflows, not suggestions.
When Something Goes Wrong
Sometimes a session misbehaves: a skill fires when it shouldn't, stays silent when it should, or the agent ignores its plan, repeats work, or burns more tokens than you'd expect. Ask your coding agent to "figure out what went wrong with superpowers in this session" and it will invoke the diagnosing-superpowers skill. To examine an earlier session, name it: "figure out what went wrong with superpowers in session <id>".
The skill reads the session transcript, reports what happened with line-level evidence, and, if you want, packages a scrubbed bundle for a bug report.
Community
Superpowers is built by Jesse Vincent and the rest of the folks at Prime Radiant.
- Discord: Join us for community support, questions, and sharing what you're building with Superpowers
- Issues: https://github.com/obra/superpowers/issues
- Release announcements: Sign up to get notified about new versions
What's Inside
Skills Library
Testing
- test-driven-development - RED-GREEN-REFACTOR cycle (includes testing anti-patterns reference)
Debugging
- systematic-debugging - 4-phase root cause process (includes root-cause-tracing, defense-in-depth, condition-based-waiting techniques)
- verification-before-completion - Ensure it's actually fixed
- diagnosing-superpowers - Work out what went wrong in a session, with evidence; export a scrubbed bundle or file an issue
Collaboration
- brainstorming - Drawing out intent before building
- writing-plans - Detailed implementation plans
- executing-plans - Inline plan execution: one context, one final review
- dispatching-parallel-agents - Concurrent subagent workflows
- requesting-code-review - Pre-review checklist
- receiving-code-review - Responding to feedback
- using-git-worktrees - Parallel development branches
- finishing-a-development-branch - Merge/PR decision workflow
- subagent-driven-development - Fast iteration with two-stage review (spec compliance, then code quality)
Meta
- writing-skills - Create new skills following best practices (includes testing methodology)
- using-superpowers - Introduction to the skills system
Philosophy
- Test-Driven Development - Write tests first, always
- Systematic over ad-hoc - Process over guessing
- Complexity reduction - Simplicity as primary goal
- Evidence over claims - Verify before declaring success
Read the original release announcement.
Contributing
The general contribution process for Superpowers is below. Keep in mind that we don't generally accept contributions of new skills and that any updates to skills must work across all of the coding agents we support. We also don't accept agent-written PRs that fix issues filed by someone else unless a maintainer asked for one, even when you hit the same problem; add your report to the existing issue instead.
- Fork the repository
- Switch to the 'dev' branch
- Create a branch for your work
- Follow the
writing-skillsskill for creating and testing new and modified skills - Submit a PR, filling in every section of the pull request template, including the summary a human writes in their own words.
Skill-behavior tests use the drill eval harness from superpowers-evals, cloned into evals/ — see evals/README.md for setup. Plugin-infrastructure tests live at tests/ and run via the relevant run-*.sh or npm test.
See skills/writing-skills/SKILL.md for the complete guide.
Updating
Superpowers updates are somewhat coding-agent dependent, but are often automatic.
License
MIT License - see LICENSE file for details
Visual companion telemetry
Because skills and plugins don't provide any feedback to creators, we have no idea how many of you are using Superpowers. By default, the Prime Radiant logo on brainstorming's optional visual companion feature is loaded from our website. It includes the version of Superpowers in use. It does not include any details about your project, prompt, or coding agent. We don't see your clicks or anything about what you're building. This helps us have a rough idea of how many folks are using Superpowers and which version of Superpowers they're using. It's 100% optional. To disable this, set the environment variable SUPERPOWERS_DISABLE_TELEMETRY to any true value. Superpowers also honors Claude Code's DISABLE_TELEMETRY and CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC opt-outs.
Questions
- Do Hugging Face Skills and Superpowers list the same price?
- Both listings report Free. Free to use. Pricing is reported by the maker and is not independently verified.
- Which has more upvotes, Hugging Face Skills or Superpowers?
- Both have 0 upvotes.
- What does each maker say Hugging Face Skills and Superpowers are for?
- Hugging Face Skills: Skills for agents to work with Hugging Face Hub, datasets, model training and evaluation. Superpowers: Skills library and development methodology for coding agents covering planning, TDD and debugging.
Hugging Face Skills alternatives·Superpowers alternatives·All coding tools