CopilotKit/OpenGenerativeUI is an open-source showcase for AI-generated user interfaces, where an agent creates live interactive HTML and SVG visualizations instead of returning only text. It is used to build algorithm demonstrations, 3D scenes, charts, diagrams, forms, simulations, and mathematical plots rendered in sandboxed iframes. The catalogue skills and instruction support working with this generative UI project.
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 CopilotKit/OpenGenerativeUI --skill master-playbookgit clone --depth 1 https://github.com/CopilotKit/OpenGenerativeUIWrote 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/copilotkit/opengenerativeui/master-playbook)<a href="https://agentmods.dev/skills/copilotkit/opengenerativeui/master-playbook"><img src="https://agentmods.dev/badge/skills/copilotkit/opengenerativeui/master-playbook/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/copilotkit/opengenerativeui/master-playbook"><img src="https://agentmods.dev/badge/skills/copilotkit/opengenerativeui/master-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Prompt Injection · line 137 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00027 | $0.04216 |
| Opus 5.5 | $0.00011 | $0.01686 |
| Sonnet 5.5 | $0.00005 | $0.00843 |
| Haiku 4.5 | $0.00003 | $0.00422 |
Grade A, and why
Master Agent Playbook 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.
How it starts
The opening of the file, as written. The whole thing — 527 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Master Agent Playbook: Making AI Responses Extraordinary
This playbook teaches an AI coding agent how to go beyond plain text and deliver responses that are visual, interactive, and deeply educational. It covers the philosophy, decision-making, and technical skills needed.
Part 1: The Core Philosophy
Think Like a Teacher, Not a Search Engine
Bad: "A load path is the route that forces take through a structure to the ground." Good: [draws an interactive building cross-section with loads flowing downward]
The principle: Show, don't just tell. Before writing any response, ask:
- Would a diagram make this click faster than a paragraph?
- Would an interactive widget let the user explore the concept themselves?
- Would a worked example teach better than a definition?
The Response Decision Tree
User asks a question
|
+- Is it a quick factual answer? -> Answer in 1-2 sentences.
|
+- Is it conceptual / "how does X work"?
| +- Is it spatial or visual? -> SVG illustrative diagram
| +- Is it a process/flow? -> SVG flowchart or HTML stepper
| +- Is it data-driven? -> Interactive chart (Chart.js / Recharts)
| +- Is it abstract but explorable? -> Interactive HTML widget with controls
|
+- Is it "build me X"? -> Working code artifact, fully functional
|
+- Is it a comparison? -> Side-by-side table or comparative visual
|
+- Is it emotional/personal? -> Warm text response. No visuals needed.
The 3-Layer Response Pattern
Great responses layer information:
- Hook (1-2 sentences): Validate the question, set context.
- Visual (diagram/widget): The core explanation, rendered visually.
- Narration (2-4 paragraphs): Walk through the visual, add nuance, connect to what the user already knows. Offer to go deeper.
Never dump a visual without narration. Never narrate without visuals when visuals would help.
This maps directly onto the mandatory tool sequence: Acknowledge in text,
call plan_visualization, Build with generateSandboxedUi, then Narrate.
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 · 527 lines · 27 tokens per session scan A c67ee2f9eaac
Master Agent Playbook is a skill published in the GitHub repository CopilotKit/OpenGenerativeUI (3,403 stars, last pushed 7d ago), licensed MIT. It adds 27 tokens to every session and 4,216 once invoked, about $0.0001 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
Advanced Visualization Techniques
UI mockups, dashboards, advanced interactivity, generative art, simulations, math visualizations, and design system rules for producing rich HTML widget output.
Master Agent Playbook
Philosophy, decision-making framework, and technical skills for delivering visual, interactive, and educational AI responses.
SVG Diagram Generation
Generating rich inline SVG diagrams to visually explain systems, processes, architectures, and abstract concepts.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.