Borrowing it
Nothing to install: this file belongs to tensorcircuit/tensorcircuit-ng. 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/tensorcircuit/tensorcircuit-ng/master/.agents/skills/demo-generator/SKILL.mdgit clone --depth 1 https://github.com/tensorcircuit/tensorcircuit-ngWrote 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/tensorcircuit/tensorcircuit-ng/demo-generator)<a href="https://agentmods.dev/skills/tensorcircuit/tensorcircuit-ng/demo-generator"><img src="https://agentmods.dev/badge/skills/tensorcircuit/tensorcircuit-ng/demo-generator/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/tensorcircuit/tensorcircuit-ng/demo-generator"><img src="https://agentmods.dev/badge/skills/tensorcircuit/tensorcircuit-ng/demo-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00048 | $0.00968 |
| Opus 5.5 | $0.00019 | $0.00387 |
| Sonnet 5.5 | $0.00010 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
Grade A, and why
demo-generator 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 2mo 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When tasked with creating a Streamlit demo from a TensorCircuit-NG (TC-NG) script, you act as a Creative Full-Stack Quantum Scientist. Your goal is to build an app that doesn't just "run the script," but makes the underlying physics alive, interactive, and surprising for an audience. Use your imagination to create a 'wow' effect.
0. App Metadata & Docstring
The generated [original_name]_app.py MUST begin with a standardized docstring:
"""
Streamlit Interactive Demo: [App Title]
Origin: Based on [path/to/original_script.py]
Usage: streamlit run [original_name]_app.py
Description: [Brief 1-sentence description of the interactivity and goal]
"""
1. Intent & Interactivity Extraction
- Identify Controllable Parameters: What variables in the script are "fun" to change? (e.g., lattice size $L$, bias $\theta$, noise rate $p$, number of qubits $N$).
- Identify Visual "Hooks": What is the most impressive result? (e.g., a 3D phase transition 'volcano', a real-time circuit animation, or a statevector magnitude heatmap).
- Be Imaginative & Surprising: Don't just settle for standard line plots. If the physics allows, create 3D surfaces, interactive phase diagrams, or evolving probability landscapes that make the user say "Wow."
- Plan the Story: How should the user interact with it? (e.g., "Adjust the temperature and watch the magnetization landscape buckle").
2. Standardized App Structure
The generated [original_name]_app.py should follow this professional blueprint:
A. Global Styling & Branding
- Use
st.set_page_configwith a wide layout and a custom title. - Integrate the official TensorCircuit-NG logo from:
https://github.com/tensorcircuit/tensorcircuit-ng/blob/master/docs/source/statics/logong.png?raw=true. - Apply custom CSS for a premium "Dark/Glassmorphism" look if possible.
B. Sidebar Controls
- Put all simulation parameters in the
st.sidebar. - Use
st.sidebar.slider,num_input, orselectbox. - Use clear labels and helpful tooltips for each parameter.
What ships with it
1 file 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.
- 2mo ago First seen · 59 lines · 48 tokens per session scan A 53515150fb95
demo-generator is a skill published in the GitHub repository tensorcircuit/tensorcircuit-ng (93 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 968 once invoked, about $0.0002 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.
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