Ai Multimodal Output by varunk130 — AI Skill for Any LLM

Q: What does the Ai Multimodal Output AI skill do?

Design how AI presents results across text, code, images, charts, and mixed media - response formatting, output hierarchy, and cross-modal transitions. Use when: AI output design, response formatting, AI results display, multimodal output, AI-generated content presentation, code output UX, AI visualization.

Q: Why use Ai Multimodal Output on TypingMind?

Because you install it once and use it with any model. Ai Multimodal Output is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Ai Multimodal Output in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/varunk130/ai-ux-skill-library/tree/main/skills/ai-multimodal-output. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Q: Which AI models can use Ai Multimodal Output?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Ai Multimodal Output?

As many as you like. As long as a model supports skills, you can use Ai Multimodal Output with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Ai Multimodal Output AI skill free?

Yes. It is published on GitHub by varunk130 under the MIT license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Overview

Publisher	varunk130
Repository	`ai-ux-skill-library`
Skill name	`ai-multimodal-output`
Stars	3
Forks	0
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions

A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

Works with any LLM

AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

Self-contained

Everything the model needs lives in the instructions — no extra files to sync.

Open source

Published by varunk130 on GitHub. Read the source before you install it.

Installation

Install the Ai Multimodal Output AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/varunk130/ai-ux-skill-library/tree/main/skills/ai-multimodal-output
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/varunk130/ai-ux-skill-library.git /tmp/ai-ux-skill-library
mkdir -p .claude/skills
cp -r /tmp/ai-ux-skill-library/skills/ai-multimodal-output .claude/skills/ai-multimodal-output

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Multimodal Output in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Multimodal Output on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Multimodal Output is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed

The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

Switch models mid-chat

Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Output & Multimodal Design

Design how AI results are presented, formatted, and consumed across every output modality - text, code, images, data visualizations, and mixed media. The RENDER framework ensures AI outputs are scannable, actionable, and trustworthy regardless of format.

Core Principle

AI generates output. Humans consume meaning. The gap between raw AI output and human understanding is a design problem, not a model problem. A brilliant AI answer poorly formatted is worse than a mediocre answer well-structured.

The RENDER Framework

Letter	Principle	Design Question
R	Right Format	Is the output in the format that best serves the user's goal (not the model's default)?
E	Easy to Scan	Can the user extract the key insight in under 5 seconds?
N	Navigable Depth	Can the user drill into details without drowning in them upfront?
D	Directly Actionable	Can the user act on this output without additional steps?
E	Editable	Can the user modify, refine, or iterate on the output in-place?
R	Reproducible	Can the user understand how to get this output again or share it?

Output Format Selection Matrix

The AI should automatically select the best output format based on the content type:

Content Type	Best Format	Why	Anti-Pattern
Single factual answer	Inline text (1-2 sentences)	Quick to consume	A 3-paragraph essay for a yes/no question
Step-by-step instructions	Numbered list with headers	Scannable, sequenceable	Prose paragraphs describing steps
Comparison	Table with clear columns	Side-by-side evaluation	Alternating paragraphs about each option
Code	Syntax-highlighted code block with language label	Copy-friendly, readable	Code embedded in prose without formatting
Data/trends	Chart or visualization	Pattern recognition	Raw numbers in a paragraph
Complex explanation	Headers + sections + summary	Navigable hierarchy	Wall of undifferentiated text
Creative writing	Clean prose, no artifacts	Immersive reading	Bullet points for a poem
Mixed	Composite card with labeled sections	Each element in its optimal format	Forcing everything into one format

The Response Hierarchy

Every AI response should follow this structure (whether the output is 1 line or 100):

Level	What It Contains	Always Visible?	Example
Headline	The core answer in one sentence or phrase	Yes	"Your churn rate increased 12% QoQ, driven by enterprise segment."
Key Details	Supporting information, top 3-5 points	Yes (brief)	Key drivers, affected metrics, comparison to benchmark
Evidence	Sources, data, methodology, reasoning	Collapsed - expand on demand	Data tables, source citations, model confidence
Actions	What the user can do next	Yes (as buttons or suggestions)	"View affected accounts" / "Generate retention plan" / "Share with team"

Rule: If the user has to scroll past the headline to understand the output, the formatting failed.

Text Output Patterns

Length Calibration

User Context	Target Length	Formatting
Chat conversation	50-150 words	Conversational paragraphs, minimal formatting
Professional analysis	200-500 words	Headers, bullet points, key metrics highlighted
Research/deep dive	500-2000 words	Table of contents, collapsible sections, executive summary
Quick reference	10-50 words	Single sentence or small table

Text Formatting Rules

Rule	Implementation	Why
Bold key numbers and conclusions	12% increase not "12% increase"	Users scan for emphasized content
Use tables for comparisons, not prose		Humans process tabular data 3x faster than narrative comparisons
Break at natural section points	New header every 3-5 paragraphs	Prevents wall-of-text fatigue
Lead with the answer, not the reasoning	Answer first, then "Here's why..."	Most users want the answer; some want the reasoning

Code Output Patterns

Pattern	When to Use	Implementation
Copy-ready block	User will paste code directly	Syntax highlighting + one-click copy button + language label
Diff view	AI modified existing code	Side-by-side or inline diff showing changes
Annotated code	Code needs explanation	Inline comments or adjacent explanation panel
Executable snippet	Code can run in-place	Run button + output preview below the code block
Multi-file output	Change spans multiple files	Tabbed interface or file tree with per-file diffs

Code Output Anti-Patterns

Pattern	Why It Fails
Code in a paragraph without code block formatting	Unreadable, uncopyable
Explaining code in prose ABOVE the code block	User reads explanation, forgets it by the time they see the code
Showing only the changed lines without surrounding context	User can't locate where to put the code
No language label on code blocks	User can't tell if it's Python, JavaScript, or pseudocode

Data Visualization Output

When AI output contains data, choose the right visualization:

Data Relationship	Visualization	When NOT to Use
Trend over time	Line chart	Fewer than 4 data points (use table instead)
Part of whole	Pie chart (< 6 slices) or stacked bar	More than 6 categories (use bar chart)
Comparison across categories	Horizontal bar chart	Time-series data (use line chart)
Distribution	Histogram or box plot	Categorical data
Correlation	Scatter plot	More than 2 variables (use small multiples)
Geographic	Map	Non-geographic data forced onto a map

Visualization Output Rules

Rule	Why
Always include a text summary alongside the visualization	Not all users are visual learners; screen readers can't read charts
Label axes and include units	"Revenue" means nothing without "$M" and time period
Show data source and recency	"Based on Q1 2026 data" builds trust; undated charts don't
Offer "View as table" toggle	Some users prefer raw numbers; tables are more accessible

Multimodal Output Composition

When AI output spans multiple modalities (text + image + code + data):

The Composite Output Card

Section	Content	Position
Summary text	1-2 sentence headline of what the AI produced	Top
Primary output	The main deliverable (image, analysis, document)	Center, largest
Supporting detail	Methodology, sources, alternative versions	Below primary, collapsible
Action bar	Copy, download, share, regenerate, iterate	Bottom, always visible

Cross-Modal Transition Design

When output switches between modalities:

Transition	Design Pattern
Text → Chart	"Here's the breakdown:" + inline chart + "Key takeaway: [text summary below chart]"
Text → Code	"Here's how to implement this:" + code block + "This does X by Y"
Text → Image	"Here's what that looks like:" + image + descriptive caption
Multiple modalities in one response	Use clear section headers and visual separators between modalities

Anti-Patterns

Pattern	Why It Fails
Defaulting to maximum length for every response	Users asked a simple question and got a dissertation
Prose when a table would work	Forces linear reading of comparative information
Charts without text summaries	Excludes screen reader users; misses users who don't read charts
Outputting raw JSON/XML to non-technical users	Data format is not information format
No copy/download affordance on generated content	Users have to manually select and copy, or take screenshots
Identical formatting for 1-line answers and 100-line analyses	One size fits no one

Quick Reference

Task	Framework Element	Key Deliverable
Design output formatting for AI product	Output Format Selection Matrix + Response Hierarchy	Format rules by content type + response structure spec
Improve readability of AI responses	Text Output Patterns	Length calibration guide + formatting rules
Design code output experience	Code Output Patterns	Code block component spec with copy, diff, and run features
Add data visualization to AI outputs	Data Visualization Output table	Chart selection guide + accessibility requirements
Design mixed-modality responses	Composite Output Card + Cross-Modal Transitions	Multimodal response template

Integration

Works with: ai-trust-transparency (confidence indicators in output), ai-error-resilience (error display formatting), ai-prompt-ux (output format matching input intent), ai-conversation-architect (response formatting in dialogue).

Frequently asked questions

What does the Ai Multimodal Output AI skill do?

Design how AI presents results across text, code, images, charts, and mixed media - response formatting, output hierarchy, and cross-modal transitions. Use when: AI output design, response formatting, AI results display, multimodal output, AI-generated content presentation, code output UX, AI visualization.

Why use Ai Multimodal Output on TypingMind?

Because you install it once and use it with any model. Ai Multimodal Output is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Multimodal Output in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/varunk130/ai-ux-skill-library/tree/main/skills/ai-multimodal-output. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ai Multimodal Output?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Multimodal Output?

As many as you like. As long as a model supports skills, you can use Ai Multimodal Output with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Multimodal Output AI skill free?

Yes. It is published on GitHub by varunk130 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AI Output & Multimodal Design

Core Principle

The RENDER Framework

Output Format Selection Matrix

The Response Hierarchy

Text Output Patterns

Length Calibration

Text Formatting Rules

Code Output Patterns

Code Output Anti-Patterns

Data Visualization Output

Visualization Output Rules

Multimodal Output Composition

The Composite Output Card

Cross-Modal Transition Design

Anti-Patterns

Quick Reference

Integration

Frequently asked questions

Related AI Skills

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