Hugging Face Skills is a collection of packaged instructions, scripts, and resources that teach AI agents how to perform tasks in the Hugging Face ecosystem, such as managing models and datasets, training models, and running evaluations. It is for coding agents that need to use Hugging Face Hub and machine-learning workflows. The catalogue entries are the project's own skills and integrations for agent clients.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add huggingface/skills --skill huggingface-lora-space-buildergit clone --depth 1 https://github.com/huggingface/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huggingface/skills/huggingface-lora-space-builder)<a href="https://agentmods.dev/skills/huggingface/skills/huggingface-lora-space-builder"><img src="https://agentmods.dev/badge/skills/huggingface/skills/huggingface-lora-space-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huggingface/skills/huggingface-lora-space-builder"><img src="https://agentmods.dev/badge/skills/huggingface/skills/huggingface-lora-space-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 64 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Data Exfiltration · line 302 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00187 | $0.07955 |
| Opus 5.5 | $0.00075 | $0.03182 |
| Sonnet 5.5 | $0.00037 | $0.01591 |
| Haiku 4.5 | $0.00019 | $0.00796 |
Grade A, and why
huggingface-lora-space-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- huggingface-lora-space-builder — 94% identical, 4 lines differ
- huggingface-lora-space-builder — 81% identical, 73 lines differ
How it starts
The opening of the file, as written. The whole thing — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gradio LoRA Space Builder
Build and publish a Gradio demo on Hugging Face Spaces that runs inference with a user-provided LoRA. Use whenever someone asks to create, generate, ship, or publish "a Space", "a demo", "a Gradio app", or "a playground" for a LoRA — whether the base model is Qwen-Image, Qwen-Image-Edit, LTX, or another diffusion model. Also use when someone describes a LoRA they trained or hosts on the Hub and wants to share it. The default target is ZeroGPU hardware and the default inference library is diffusers when the base model supports it.
The output is a real, published Space (private by default) that the user can try in the browser, not a local script.
What "good" looks like for these demos
The demo should feel handcrafted for this specific LoRA, not a generic template with the LoRA bolted on. Two LoRAs that share a task can still need different demos: a pose-control video LoRA and an outpainting video LoRA both take video in and produce video out, but the inputs the user provides, the preprocessing, and the controls are completely different. Recognizing that is the central job here.
Concretely, a good demo:
- Loads fast and runs fast — minimal model loading, sensible step count, no wasted computation per call.
- Has a UI with exactly the controls this LoRA needs and nothing else. Excess sliders are a cost, not a feature.
- Shows the user what's happening — progress, intermediate outputs where useful, the seed used, a clear error when input is missing.
- Honors the LoRA's own recommendations from its model card: trigger words, recommended step count, recommended guidance scale, recommended LoRA scale, example inputs.
- Is creative where creativity helps — interactive canvases, before/after sliders, side-by-side previews of intermediate processing — and plain where plainness is right.
Workflow
Work through these phases in order. Information gathered in one phase decides the next.
- Gather the LoRA info needed to pick a pipeline and design a UI.
- Pick the base pipeline and inference recipe.
- Design the UI for this specific LoRA's task and inputs.
- Write
app.py,requirements.txt, andREADME.mdtogether; show all three to the user for one batched approval. - Publish the Space (private).
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 394 lines · 187 tokens per session scan A 7192d4a44172
huggingface-lora-space-builder is a skill published in the GitHub repository huggingface/skills (11,203 stars, last pushed yesterday), licensed Apache-2.0. It adds 187 tokens to every session and 7,955 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
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training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…