demo-generator

demo-generator is a skill for Claude Code, Codex from tensorcircuit/tensorcircuit-ng. It costs 48 tokens per session (968 once invoked), scanned A, original, Apache-2.0.

A tool that turns TensorCircuit-NG scripts into interactive Streamlit applications. TensorCircuit-NG is used for quantum-circuit and physics simulations, while Streamlit creates web interfaces in Python.

In plain words
What is it for?
Use it to create demos with interactive widgets, real-time charts, circuit animations, heatmaps, or other visual views of a simulation.
Why use it?
It makes simulation parameters adjustable and presents selected results through live controls and visualizations.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tensorcircuit/tensorcircuit-ng/demo-generator
Any agent
npx skills add tensorcircuit/tensorcircuit-ng --skill demo-generator
Clone the repo
git clone --depth 1 https://github.com/tensorcircuit/tensorcircuit-ng

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for demo-generator

README.md
[![agentmods](https://agentmods.dev/badge/skills/tensorcircuit/tensorcircuit-ng/demo-generator.svg)](https://agentmods.dev/skills/tensorcircuit/tensorcircuit-ng/demo-generator)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 968 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.00968
Opus 5 $0.00024 $0.00484
Sonnet 5 $0.00010 $0.00194
Haiku 4.5 $0.00005 $0.00097

Measured 6d ago against content hash 53515150fb95, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 6d 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.

.agents/skills/demo-generator/SKILL.md · 59 lines

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_config with 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, or selectbox.
  • Use clear labels and helpful tooltips for each parameter.

Read the full file on GitHub · 59 lines

Files

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.

Changes

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.

  1. 6d ago First seen · 59 lines · 48 tokens per session scan A 53515150fb95

Subscribe to this mod's changes

demo-generator is a skill published in the GitHub repository tensorcircuit/tensorcircuit-ng (89 stars, last pushed 4d ago), licensed Apache-2.0. It adds 48 tokens to every session and 968 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

physicsnemo-discover

Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…

NVIDIA/physicsnemo · 124 tokens

fine-tuning-serving-openpi

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging…

Orchestra-Research/AI-Research-SKILLs · 87 tokens

cellxgene-census-query

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…

PharMolix/OpenBioMed · 105 tokens

start-experiment

Start the autoresearch optimization loop for a specific model + lane. Resolves the hierarchical program.md (root → model → lane), asks the user for hardware (local TPU VM or GKE cluster of a specified TPU type + topology), discovers available clusters from .env/, checks occupancy with USERPREFIX-aware attribution…

vlasenkoalexey/tpu_performance_autoresearch_wiki · 103 tokens

stop-experiment

Stop the autoresearch optimization loop cleanly. Disarms the launch-armed process watcher, cancels pending wakeups, reaps orphan workloads on the project's cluster pool, files any missing experiment pages, runs lint, and appends a clean-shutdown marker to wiki/log.md. Invoke at the end of an autoresearch session.

vlasenkoalexey/tpu_performance_autoresearch_wiki · 70 tokens

create-experiment

Bootstrap a new model family for the autoresearch loop. Creates the folder structure under wiki/experiments/, scaffolds the model-level program.md from a template (asking the user about lanes, sizes, hardware targets, sequence length, and architecture invariants), optionally creates lane-level program.md stubs, and…

vlasenkoalexey/tpu_performance_autoresearch_wiki · 90 tokens