Data Analytics

OrganizationPopular

markdown-viewer

data-analytics

Create data pipeline and analytics architecture diagrams using PlantUML syntax with database/analytics stencil icons. Best for ETL pipelines, data lakes, real-time streaming, data warehousing, and BI dashboard design.

Overview

Publisher	markdown-viewer
Repository	`skills`
Skill name	`data-analytics`
Stars	3.3K
Forks	190
Bundled files	8
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.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by markdown-viewer on GitHub. Read the source before you install it.

Installation

Install the Data Analytics 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.
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/markdown-viewer/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/data-analytics .claude/skills/data-analytics

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

Use it in TypingMind

Enable Data Analytics 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 Data Analytics 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 Data Analytics 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.

Data Analytics Diagram Generator

Quick Start: Define data sources → Declare ingestion/ETL icons → Connect to storage/warehouse → Add BI/visualization → Wrap in ```plantuml fence.

⚠️ IMPORTANT: Always use ```plantuml or ```puml code fence. NEVER use ```text — it will NOT render as a diagram.

Critical Rules

Every diagram starts with @startuml and ends with @enduml
Use left to right direction for data pipelines (Source → Ingest → Transform → Store → Visualize)
Use mxgraph.aws4.* stencil syntax for analytics, database, and storage icons
Default colors are applied automatically — you do NOT need to specify fillColor or strokeColor
Use rectangle "Zone" { ... } or package "Layer" { ... } for grouping pipeline stages
Directed flows use -->, async/streaming flows use ..> (dashed)

Full stencil reference: See stencils/README.md for 9500+ available icons.

Mxgraph Stencil Syntax


mxgraph.aws4.<icon> "Label" as <alias>

Analytics & ETL Stencils

Category	Stencils	Purpose
Query Engine	`athena`, `athena_data_source_connectors`	Serverless SQL on S3 data
ETL	`glue`, `glue_crawlers`, `glue_data_catalog`, `aws_glue_data_quality`, `aws_glue_for_ray`	Data integration & cataloging
Streaming	`kinesis`, `kinesis_data_streams`, `kinesis_data_firehose`, `kinesis_data_analytics`, `kinesis_video_streams`	Real-time data streaming
MapReduce	`emr`, `emr_engine`, `emr_engine_mapr_m3`, `emr_engine_mapr_m5`	Big data processing (Spark, Hive)
Data Warehouse	`redshift`, `redshift_ra3`, `redshift_streaming_ingestion`, `redshift_ml`	Columnar analytics warehouse
Search	`opensearch_service_data_node`, `opensearch_ingestion`, `cloudsearch`	Full-text search & log analytics
BI	`quicksight`	Dashboards & visualizations
Data Lake	`lake_formation`, `s3`, `glacier`, `glacier_deep_archive`	Governed data lake storage
Catalog	`datazone_custom_asset_type`, `data_exchange`	Data governance & sharing
Streaming Kafka	`msk`, `msk_connect`	Managed Kafka streaming

Database Stencils

Category	Stencils	Purpose
Relational	`aurora`, `aurora_instance`, `rds`, `rds_instance`, `rds_mysql_instance`, `rds_postgresql_instance`	Transactional databases
NoSQL	`dynamodb`, `dynamodb_table`, `dynamodb_global_secondary_index`, `dynamodb_stream`	Key-value & document store
Graph	`neptune`	Graph database
In-Memory	`elasticache`, `elasticache_for_redis`, `elasticache_for_memcached`	Cache & session store
Document	`documentdb`, `documentdb_with_mongodb_compatibility`	Document database
Ledger	`quantum_ledger_database`	Immutable transaction log
Wide-Column	`keyspaces`	Cassandra-compatible

Connection Types

Syntax	Meaning	Use Case
`A --> B`	Solid arrow	Batch data flow / API call
`A ..> B`	Dashed arrow	Streaming / async / CDC
`A -- B`	Solid line	Bidirectional sync
`A --> B : "label"`	Labeled connection	Describe data format or volume

Quick Example

plantuml
@startuml
left to right direction
mxgraph.aws4.s3 "Data Lake\n(S3)" as s3
mxgraph.aws4.glue "Glue\nETL" as glue
mxgraph.aws4.redshift "Redshift" as rs
mxgraph.aws4.quicksight "QuickSight" as qs

s3 --> glue
glue --> rs
rs --> qs
@enduml

Data Analytics Architecture Types

Type	Purpose	Key Stencils	Example
Data Lake	Centralized raw data store	`s3`, `lake_formation`, `glue`, `athena`	data-lake.md
Real-time Streaming	Event stream processing	`kinesis`, `msk`, `lambda_function`, `opensearch_service`	real-time-streaming.md
Data Warehouse	Star-schema analytics	`redshift`, `glue`, `quicksight`	data-warehouse.md
ETL Pipeline	Extract-transform-load	`glue`, `glue_crawlers`, `glue_data_catalog`, `s3`	etl-pipeline.md
Log Analytics	Centralized logging	`kinesis_data_firehose`, `opensearch_service`, `lambda_function`	log-analytics.md
ML Feature Store	Feature engineering pipeline	`glue`, `s3`, `athena`, `emr`	ml-feature-pipeline.md
CDC Pipeline	Database change capture	`dynamodb_streams`, `kinesis`, `lambda_function`, `redshift`	cdc-pipeline.md
Multi-source BI	Cross-database reporting	`aurora`, `dynamodb`, `redshift`, `quicksight`	multi-source-bi.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Analytics AI skill do?

Why use Data Analytics on TypingMind?

Because you install it once and use it with any model. Data Analytics 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 Data Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/markdown-viewer/skills/tree/main/data-analytics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Analytics?

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 Data Analytics?

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

Is the Data Analytics AI skill free?

It is published on GitHub by markdown-viewer. Check the repository for licensing terms. 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.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Analytics Diagram Generator

Critical Rules

Mxgraph Stencil Syntax

Analytics & ETL Stencils

Database Stencils

Connection Types

Quick Example

Data Analytics Architecture Types

Bundled files

Frequently asked questions

Related AI Skills

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