Gradio is a Python package for building web interfaces around machine learning models, APIs, or ordinary Python functions. It is used by developers and data scientists to create and share interactive demos without writing frontend code.
Borrowing it
Nothing to install: this file belongs to gradio-app/gradio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gradio-app/gradio/main/.agents/skills/hf-gradio/SKILL.mdgit clone --depth 1 https://github.com/gradio-app/gradioWrote 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/gradio-app/gradio/hf-gradio)<a href="https://agentmods.dev/skills/gradio-app/gradio/hf-gradio"><img src="https://agentmods.dev/badge/skills/gradio-app/gradio/hf-gradio/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/gradio-app/gradio/hf-gradio"><img src="https://agentmods.dev/badge/skills/gradio-app/gradio/hf-gradio.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00055 | $0.00704 |
| Opus 5 | $0.00028 | $0.00352 |
| Sonnet 5 | $0.00011 | $0.00141 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
hf-gradio 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hf-gradio CLI Skill
The hf-gradio CLI gradio CLI includes info and predict commands for interacting with Gradio apps programmatically.
Step 1 - Verify installation
Verify that either hf-gradio or gradio are installed in the current virtual environment.
If the hf CLI app is installed. The hf-gradio extension can be installed via
hf extensions install gradio-app/hf-gradio
Step 2 - Use info to discover endpoints and payload format
gradio info <space_id_or_url>
hf-gradio info <space_id_or_url>
hf gradio info <space_id_or_url>
Returns a JSON payload describing all endpoints, their parameters (with types and defaults), and return values.
gradio info gradio/calculator
# {
# "/predict": {
# "parameters": [
# {"name": "num1", "required": true, "default": null, "type": {"type": "number"}},
# {"name": "operation", "required": true, "default": null, "type": {"enum": ["add", "subtract", "multiply", "divide"], "type": "string"}},
# {"name": "num2", "required": true, "default": null, "type": {"type": "number"}}
# ],
# "returns": [{"name": "output", "type": {"type": "number"}}],
# "description": ""
# }
# }
File-type parameters show "type": "filepath" with instructions to include "meta": {"_type": "gradio.FileData"} — this signals the file will be uploaded to the remote server.
Step 3 - Use predict to generate the prediction
gradio predict <space_id_or_url> <endpoint> <json_payload>
hf-gradio predict <space_id_or_url> <endpoint> <json_payload>
hf gradio predict <space_id_or_url> <endpoint> <json_payload>
Returns a JSON object with named output keys.
# Simple numeric prediction
gradio predict gradio/calculator /predict '{"num1": 5, "operation": "multiply", "num2": 3}'
# {"output": 15}
# Image generation
gradio predict black-forest-labs/FLUX.2-dev /infer '{"prompt": "A majestic dragon"}'
# {"Result": "/tmp/gradio/.../image.webp", "Seed": 1117868604}
# File upload (must include meta key)
gradio predict gradio/image_mod /predict '{"image": {"path": "/path/to/image.png", "meta": {"_type": "gradio.FileData"}}}'
# {"output": "/tmp/gradio/.../output.png"}
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.
- 12d ago First seen · 83 lines · 55 tokens per session scan A 2be3bc11a452
hf-gradio is a skill published in the GitHub repository gradio-app/gradio (43,523 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 704 once invoked, about $0.0003 per session on Opus 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.
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