narrative-text-visualization
antvis/chart-visualization-skills
Generate structured narrative text visualizations from data using T8 Syntax for data interpretation reports and articles.
What is narrative-text-visualization?
This skill transforms data into structured narrative text visualizations using T8 Syntax, a declarative Markdown-like language with semantic entity annotations. Use it when you need to create data interpretation reports, summaries, or structured articles that combine natural language with annotated data points and mini visualizations.
- Create narrative text with semantic entity annotations for data points
- Generate professional data reports with built-in mini charts (pie, line)
- Support multiple frameworks (React, Vue, vanilla JavaScript)
- Apply standardized styling automatically to text visualizations
- Annotate metrics, trends, dimensions, and comparative data with metadata
- Render lightweight, LLM-friendly declarative syntax (under 20KB gzipped)
How to install narrative-text-visualization
npx skills add https://github.com/antvis/chart-visualization-skills --skill narrative-text-visualization- Node.js and npm installed
- Basic understanding of Markdown syntax
- Familiarity with JSON for entity metadata
How to use narrative-text-visualization
- 1.Install the skill using: npx skills add https://github.com/antvis/chart-visualization-skills --skill narrative-text-visualization
- 2.Analyze the user's data and narrative requirements (topic, type, key insights)
- 3.Write T8 Syntax content with proper document structure (headings, paragraphs, lists)
- 4.Annotate all meaningful data points using entity syntax: [displayText](entityType, metadata)
- 5.Choose appropriate entity types (metric_value, ratio_value, dim_value, trend_desc, etc.) for each data point
- 6.Generate frontend code (HTML, React, or Vue) to render the T8 content
- 7.Validate that content meets minimum length, uses authentic data sources, and has proper entity annotations
Use cases
- Generate quarterly business performance reports with annotated revenue, growth rates, and trend descriptions
- Create data-driven news articles or summaries with highlighted metrics and dimensional breakdowns
- Build insight-based narratives that combine text analysis with semantic tagging of key findings
- Produce structured market analysis documents with entity annotations for rankings, anomalies, and correlations
- Transform raw data into readable executive summaries with visual hierarchy and professional formatting
- Data analysts creating insight reports
- Business intelligence professionals building narratives
- Content creators generating data-driven articles
- AI agents generating structured data summaries
- Teams building dashboard or reporting applications
narrative-text-visualization FAQ
T8 is a declarative Markdown-like language for creating narrative text visualizations with semantic entity annotations. It combines readable text with metadata-tagged data points to create professional data narratives without manual DOM construction.
Yes, T8 is framework-agnostic and works with React, Vue, or vanilla JavaScript. You generate T8 content and then create frontend code in your preferred framework to render it.
Use metric_value for main numbers, ratio_value for percentages, delta_value for changes, dim_value for categories, time_desc for periods, trend_desc for patterns, and rank/anomaly/association for comparative or unusual data.
Use the syntax [displayText](entityType, key1=value1, key2=value2). Numbers and booleans are written directly, while strings must be wrapped in double quotes (e.g., origin=1500000, unit="元").
Aim for at least 800 words or equivalent content to ensure comprehensive data narratives with sufficient detail and context.
Full instructions (SKILL.md)
Source of truth, from antvis/chart-visualization-skills.
name: narrative-text-visualization description: Generate structured narrative text visualizations from data using T8 Syntax. Use when users want to create data interpretation reports, summaries, or structured articles with semantic entity annotations. T8 is designed for unstructured data visualization where T stands for Text and 8 represents a byte of 8 bits, symbolizing deep insights beneath the text.
Narrative Text Visualization Skill
This skill provides a workflow for transforming data into structured narrative text visualizations using T8 Syntax - a declarative Markdown-like language for creating data narratives with semantic entity annotations.
What is T8
T8 is a text visualization solution under the AntV technology stack designed specifically for insight-based narrative text display. Instead of manually constructing DOM elements, you write simple, human-readable syntax that describes your data narrative.
Key Features:
- LLM-Friendly: The syntax is intuitive and can be easily generated by AI models
- Declarative & Readable: Write what you want, not how to build it
- Framework Agnostic: Works with React, Vue, or vanilla JavaScript
- Standardized Styling: Professional appearance by default
- Built-in Data Visualizations: Mini charts (pie, line) are native to the syntax
- Lightweight: Less than 20KB before gzip
Workflow
To generate narrative text visualizations, follow these steps:
1. Understand the Requirements
Analyze the user's request to determine:
- The topic or data to be analyzed
- The type of narrative needed (report, summary, article)
- The key insights to highlight
- Any specific data sources or metrics
2. Generate T8 Syntax Content
Create narrative text using T8 Syntax following the specification below. The content must include:
- Proper document structure (headings, paragraphs, lists)
- Entity annotations for all meaningful data points
- Appropriate metadata for entities (origin, assessment, etc.)
3. Generate Frontend Code
Create HTML, React, or Vue code to render the T8 content based on user's preferred framework.
4. Validate Output
Ensure:
- All data is from authentic sources
- Minimum content length (800 words or equivalent)
- Proper entity annotations throughout
- Clear structure and logical flow
T8 Syntax Specification
T8 Syntax is a Markdown-like language for creating narrative text with semantic entity annotations. It makes data analysis reports more expressive and visually appealing.
Document Structure
Headings (6 levels)
Use standard Markdown heading syntax:
# Level 1 Heading (Main Title)
## Level 2 Heading (Section)
### Level 3 Heading (Subsection)
#### Level 4 Heading
##### Level 5 Heading
###### Level 6 Heading
Rules:
- Each heading must be on its own line
- Add one space after the
#symbols - Headings create visual hierarchy in the rendered output
Paragraphs
Regular text paragraphs are separated by blank lines:
This is the first paragraph with some content.
This is the second paragraph, separated by a blank line.
Rules:
- Paragraphs can span multiple lines
- Use blank lines to separate distinct paragraphs
- Text within a paragraph flows naturally
Lists
T8 Syntax supports both unordered and ordered lists.
Unordered Lists:
- First item
- Second item
- Third item
Ordered Lists:
1. First step
2. Second step
3. Third step
Rules:
- Each list item must be on its own line
- Add one space after the bullet marker (
-,*) or number - Lists can contain entities and text formatting
Text Formatting
T8 Syntax supports inline text formatting using Markdown syntax:
Bold Text: This is **bold text** that stands out.
Italic Text: This is *italic text* for emphasis.
Underline Text: This is __underlined text__ for importance.
Links: Visit [our website](https://example.com) for more information.
Rules:
- Formatting markers must be balanced (opening and closing)
- Formatting can be combined with entities
- Links use
[text](URL)syntax where URL starts withhttp://,https://, or/
Entity Annotation Syntax
The core feature of T8 Syntax is entity annotation - marking specific data points with semantic meaning and metadata.
Basic Entity Syntax
[displayText](entityType)
displayText: The text shown to readersentityType: The semantic type of this entity
Example:
The [sales revenue](metric_name) reached [¥1.5 million](metric_value) this quarter.
Entity with Metadata
[displayText](entityType, key1=value1, key2=value2, key3="string value")
Metadata Rules:
- Separate multiple metadata fields with commas
- Numbers and booleans: write directly (e.g.,
origin=1500000,active=true) - Strings: wrap in double quotes (e.g.,
unit="元",region="Asia")
Example:
Revenue grew by [15.3%](ratio_value, origin=0.153, assessment="positive") compared to last year.
Entity Types Reference
Use these entity types to annotate different kinds of data:
| Entity Type | Description | When to Use | Examples |
|---|---|---|---|
metric_name | Name of a metric or KPI | When mentioning what you're measuring | "revenue", "user count", "market share" |
metric_value | Primary metric value | The main number/value being reported | "¥1.5 million", "50,000 users", "250 units" |
other_metric_value | Secondary or supporting metric value | Additional metrics that provide context | "average order value: $120" |
delta_value | Absolute change/difference | When showing numeric change between periods | "+1,200 units", "-$50K", "increased by 500" |
ratio_value | Percentage change/rate | When showing percentage change | "+15.3%", "-5.2%", "grew 23%" |
contribute_ratio | Contribution percentage | When showing what % something contributes | "accounts for 45%", "represents 30% of total" |
trend_desc | Trend description | Describing direction/pattern of change | "steadily rising", "declining trend", "stable" |
dim_value | Dimensional value/category | Geographic, categorical, or segmentation data | "North America", "Enterprise segment", "Q3" |
time_desc | Time period or timestamp | When specifying when something occurred | "Q3 2024", "January-March", "fiscal year 2023" |
proportion | Proportion or ratio | When expressing parts of a whole | "3 out of 5", "60% of customers" |
rank | Ranking or position | When indicating order or position in a list | "ranked 1st", "top 3", "5th place" |
difference | Comparative difference | When highlighting difference between two items | "difference of $50K", "gap of 200 units" |
anomaly | Unusual or unexpected value | When pointing out outliers or anomalies | "unusual spike", "unexpected drop" |
association | Relationship or correlation | When describing connections between metrics | "strongly correlated", "linked to", "related" |
distribution | Data distribution pattern | When describing how data is spread | "evenly distributed", "concentrated in", "spread across" |
seasonality | Seasonal pattern or trend | When describing recurring seasonal patterns | "seasonal peak", "holiday period", "Q4 surge" |
Common Metadata Fields
Add these optional fields to provide richer data context:
origin (number)
The raw numerical value behind the displayed text.
Examples:
[¥1.5M](metric_value, origin=1500000)[23.7%](ratio_value, origin=0.237)[5.2K users](metric_value, origin=5200)[3 out of 4](proportion, origin=0.75)
Why use it: Enables data visualization, sorting, and calculations
assessment (string)
Evaluates whether a change is positive, negative, or neutral.
Valid values: "positive", "negative", "equal", "neutral"
Examples:
[increased 15%](ratio_value, assessment="positive")[dropped 8%](ratio_value, assessment="negative")[remained flat](trend_desc, assessment="equal")
Why use it: Enables visual indicators (colors, icons) for good/bad trends
unit (string)
The unit of measurement for the value.
Examples:
[¥1,500,000](metric_value, unit="元", origin=1500000)[150](metric_value, unit="units")
detail (any)
Additional context or breakdown data for chart rendering. Required for certain entity types.
Required for these entity types:
rank: Array of numbers representing ranking data- Example:
[top performer](rank, detail=[5, 8, 12, 15, 20])
- Example:
difference: Array of numbers showing comparative values- Example:
[gap narrowing](difference, detail=[100, 80, 60, 40])
- Example:
anomaly: Array of numbers highlighting outliers- Example:
[unusual spike](anomaly, detail=[10, 12, 11, 45, 13])
- Example:
association: Array of {x, y} objects for correlation data- Example:
[strong correlation](association, detail=[{"x":1,"y":2},{"x":2,"y":4},{"x":3,"y":6}])
- Example:
distribution: Array of numbers showing data spread- Example:
[uneven distribution](distribution, detail=[5, 15, 45, 25, 10])
- Example:
seasonality: Object with data array and optional range- Example:
[Q4 peak](seasonality, detail={"data":[10,12,15,30],"range":[0,40]})
- Example:
Optional for other types:
[steady growth](trend_desc, detail=[100, 120, 145, 180, 210])
Data Requirements
Critical: All data must be from publicly authentic sources:
- Official announcements/financial reports
- Authoritative media (Reuters, Bloomberg, TechCrunch, etc.)
- Industry research institutions (IDC, Canalys, Counterpoint Research, etc.)
- Never use fictional, AI-guessed, or simulated data
- Use specific numbers (e.g., "146 million units", "7058 units"), not vague approximations
Complete T8 Syntax Example
# 2024 Smartphone Market Analysis
## Market Overview
Global [smartphone shipments](metric_name) reached [1.2 billion units](metric_value, origin=1200000000) in [2024](time_desc), showing a [modest decline of 2.1%](ratio_value, origin=-0.021, assessment="negative") year-over-year.
The **premium segment** (devices over $800) showed *remarkable* [resilience](trend_desc, assessment="positive"), growing by [5.8%](ratio_value, origin=0.058, assessment="positive"). [Average selling price](other_metric_value) was [$420](metric_value, origin=420, unit="USD").
## Key Findings
1. [Asia-Pacific](dim_value) remains the __largest market__
2. [Premium devices](dim_value) showed **strong growth**
3. Budget segment faced *headwinds*
## Regional Breakdown
### Asia-Pacific
[Asia-Pacific](dim_value) remains the largest market with [680 million units](metric_value, origin=680000000) shipped, though this represents a [decline of 180 million units](delta_value, origin=-180000000, assessment="negative") from the previous year.
Key markets:
- [China](dim_value): [320M units](metric_value, origin=320000000) - down [8.5%](ratio_value, origin=-0.085, assessment="negative"), [ranked 1st](rank, detail=[320, 180, 90, 65, 45]) globally, accounting for [47%](contribute_ratio, origin=0.47, assessment="positive") of regional sales
- [India](dim_value): [180M units](metric_value, origin=180000000) - up [12.3%](ratio_value, origin=0.123, assessment="positive"), [ranked 2nd](rank, detail=[320, 180, 90, 65, 45])
- [Southeast Asia](dim_value): [180M units](metric_value, origin=180000000) - [stable](trend_desc, assessment="equal")
For detailed methodology, visit [our research page](https://example.com/methodology).
Using T8 in HTML, React, and Vue
Using in HTML (via CDN)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>T8 Narrative Text</title>
</head>
<body>
<div id="container"></div>
<!-- Import T8 from unpkg CDN -->
<script src="https://unpkg.com/@antv/t8/dist/t8.min.js"></script>
<script>
// T8 is available as a global variable
const { Text } = window.T8;
// Initialize T8 instance
const text = new Text(document.getElementById('container'));
// Render narrative text using T8 Syntax
const narrativeText = `
# Sales Report
This quarter, [bookings](metric_name) are higher than usual. They are [¥348k](metric_value, origin=348.12).
[Bookings](metric_name) are up [¥180.3k](delta_value, assessment="positive") relative to the same time last quarter.
`;
text.theme('light').render(narrativeText);
</script>
</body>
</html>
Installation:
npm install @antv/t8
# or
yarn add @antv/t8
Using in React
import { Text } from '@antv/t8';
import { useEffect, useRef } from 'react';
function T8Component() {
const containerRef = useRef<HTMLDivElement>(null);
useEffect(() => {
if (!containerRef.current) return;
// Initialize T8 instance
const text = new Text(containerRef.current);
// Render narrative text using T8 Syntax
const narrativeText = `
# Sales Report
This quarter, [bookings](metric_name) are higher than usual. They are [¥348k](metric_value, origin=348.12).
[Bookings](metric_name) are up [¥180.3k](delta_value, assessment="positive") relative to the same time last quarter.
`;
text.theme('light').render(narrativeText);
// Cleanup on unmount
return () => {
text.unmount();
};
}, []);
return <div ref={containerRef} />;
}
export default T8Component;
Using in Vue 3
<template>
<div ref="containerRef"></div>
</template>
<script setup lang="ts">
import { Text } from '@antv/t8';
import { ref, onMounted, onBeforeUnmount } from 'vue';
const containerRef = ref<HTMLDivElement>();
let textInstance: Text | null = null;
onMounted(() => {
if (!containerRef.value) return;
// Initialize T8 instance
textInstance = new Text(containerRef.value);
// Render narrative text using T8 Syntax
const narrativeText = `
# Sales Report
This quarter, [bookings](metric_name) are higher than usual. They are [¥348k](metric_value, origin=348.12).
[Bookings](metric_name) are up [¥180.3k](delta_value, assessment="positive") relative to the same time last quarter.
`;
textInstance.theme('light').render(narrativeText);
});
onBeforeUnmount(() => {
if (textInstance) {
textInstance.unmount();
}
});
</script>
Using in Vue 2
<template>
<div ref="container"></div>
</template>
<script>
import { Text } from '@antv/t8';
export default {
name: 'T8Component',
data() {
return {
textInstance: null,
};
},
mounted() {
// Initialize T8 instance
this.textInstance = new Text(this.$refs.container);
// Render narrative text using T8 Syntax
const narrativeText = `
# Sales Report
This quarter, [bookings](metric_name) are higher than usual. They are [¥348k](metric_value, origin=348.12).
[Bookings](metric_name) are up [¥180.3k](delta_value, assessment="positive") relative to the same time last quarter.
`;
this.textInstance.theme('light').render(narrativeText);
},
beforeDestroy() {
if (this.textInstance) {
this.textInstance.unmount();
}
},
};
</script>
Writing Guidelines and Best Practices
Content Requirements
- Minimum Length: No less than 800 words (adjust based on data complexity)
- Structure: Clear hierarchy with logical flow between sections
- Analysis: Don't just list numbers - explain their significance and context
- Tone: Natural, fluent, objective, and professional
- Entity Usage: Annotate ALL meaningful data points - metrics, values, trends, times, changes, percentages
Entity Annotation Best Practices
- Be Comprehensive: Mark all quantitative data, not just major figures
- Use Appropriate Types: Choose the entity type that best describes the semantic meaning
- Add Metadata: Include
origin,assessment, and other relevant fields when applicable - Natural Flow: Entities should blend seamlessly into readable prose
What to Annotate
✅ DO annotate:
- All numeric values (revenue, counts, measurements)
- All percentages (changes, contributions, proportions)
- Metric names and KPIs
- Time periods
- Geographic regions and categories
- Trend descriptions
- Comparisons and changes
❌ DON'T annotate:
- Generic text without specific data meaning
- Connecting phrases and transitions
- Context that doesn't represent measurable concepts
Output Format
When generating T8 Syntax content for the user:
- Output the T8 Syntax content directly without wrapping in code blocks
- Provide the frontend code (HTML/React/Vue) based on user preference
- Ensure all entities are properly annotated with appropriate metadata
- Verify that content meets minimum length and quality requirements
The rendered output provides:
- Rich semantic markup for data entities
- Interactive entity highlighting
- Clear visual hierarchy
- Professional report-style formatting
- Responsive design for all devices
Reference Links
- T8 GitHub Repository: https://github.com/antvis/T8
- T8 Documentation: https://github.com/antvis/T8/blob/main/site/en/tutorial/quick-start.md
- T8 Syntax Reference: https://github.com/antvis/T8/blob/main/prompt.md
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