Liam ERD
Liam ERD has an llms.txt. Do you?
Liam ERD is your go-to tool for effortlessly crafting stunning ER diagrams. With user-friendly documentation and a wealth of community resources, it empowers developers to visualize database structures like never before. Dive into the world of easy diagramming!
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53 lines -95%
2 sections -88%
2 files
Liam ERD's llms.txt Insights
Short and sweet
2 sections. Minimalist, but hey — at least they showed up.
Goldilocks zone
53 lines — not too long, not too short. AI loves this.
Double trouble
Runs both llms.txt and llms-full.txt. Someone takes this seriously.
What's inside Liam ERD's llms.txt
Liam ERD's llms.txt contains 3 sections:
- Liam ERD
- Docs
- Optional
How does Liam ERD's llms.txt compare?
| Liam ERD | Directory Avg | Top Performer | |
|---|---|---|---|
| Lines | 53 | 1029 | 163,447 |
| Sections | 2 | 17 | 3207 |
Cool table. Now the real question — where do you land? Find out →
Liam ERD's llms.txt preview
First 53 of 53 lines
# Liam ERD
## Docs
- [Welcome to Liam ERD](https://liam-docs-sigma.vercel.app/docs/): Liam ERD is a tool that effortlessly generates beautiful and easy-to-read ER diagrams.
- [Liam ERD CLI](https://liam-docs-sigma.vercel.app/docs/cli)
- [CI/CD Integration](https://liam-docs-sigma.vercel.app/docs/cli/ci-cd)
- [Community Resources](https://liam-docs-sigma.vercel.app/docs/community-resources): A collection of community-created resources and articles about Liam ERD
- [Contributing](https://liam-docs-sigma.vercel.app/docs/contributing): Learn how to contribute to Liam ERD
- [Architecture Decision Records](https://liam-docs-sigma.vercel.app/docs/contributing/adr): Documented architecture decisions for the Liam project
- [20241003 - Use CSS Modules for Styling](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241003-use-css-modules-for-styling)
- [20241112 - Use React Flow for ERD Visualization](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241112-use-react-flow-for-erd-visualization)
- [20241128 - Use Fumadocs for Documentation Site](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241128-use-fumadocs-for-documentation-site)
- [20241128 - Use libpg-query for PostgreSQL SQL Parsing](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241128-use-libpg-query-for-postgresql-sql-parsing)
- [20241203 - Use Prism for schema.rb Parsing](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241203-use-prism-for-schema-rb-parsing)
- [20241206 - Node.js-Based Unified DB Schema Parsing](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20241206-node-js-based-unified-db-schema-parsing)
- [20250116 - Use DMMF for Prisma Schema Parsing](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20250116-use-dmmf-for-prisma-schema-parsing)
- [20250205 - Use fuse.js](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20250205-use-fuse-js)
- [20250421 - Apply RLS to All Tables with Organization-based Policies](https://liam-docs-sigma.vercel.app/docs/contributing/adr/20250421-apply-rls-to-all-tables-with-organization-based-policies)
- [ADR Template](https://liam-docs-sigma.vercel.app/docs/contributing/adr/template)
- [Repository Architecture](https://liam-docs-sigma.vercel.app/docs/contributing/repository-architecture): This document provides a detailed overview of our repository structure, architecture, and development workflow.
- [Parser](https://liam-docs-sigma.vercel.app/docs/parser)
- [Supported Formats](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats)
- [BigQuery](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/bigquery)
- [ClickHouse](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/clickhouse)
- [Cloud Spanner](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/cloudspanner)
- [Django ORM](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/django)
- [Drizzle](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/drizzle)
- [Amazon DynamoDB](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/dynamodb)
- [MariaDB](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/mariadb)
- [MongoDB](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/mongodb)
- [Microsoft SQL Server](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/mssql)
- [MySQL](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/mysql)
- [PostgreSQL](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/postgresql)
- [Prisma](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/prisma)
- [schema.rb (Ruby on Rails)](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/rails)
- [Amazon Redshift](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/redshift)
- [Snowflake](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/snowflake)
- [SQLite](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/sqlite)
- [tbls](https://liam-docs-sigma.vercel.app/docs/parser/supported-formats/tbls)
- [Troubleshooting Parser](https://liam-docs-sigma.vercel.app/docs/parser/troubleshooting)
- [UI Features](https://liam-docs-sigma.vercel.app/docs/ui-features): Overview of user interface features in Liam ERD
- [Browsing Your Schema](https://liam-docs-sigma.vercel.app/docs/ui-features/browsing-your-schema): >
- [Command Palette](https://liam-docs-sigma.vercel.app/docs/ui-features/command-palette): >
- [Sharing & Query Parameters](https://liam-docs-sigma.vercel.app/docs/ui-features/sharing-and-query-params)
- [Liam ERD Web](https://liam-docs-sigma.vercel.app/docs/web)
- [Troubleshooting Web Version](https://liam-docs-sigma.vercel.app/docs/web/troubleshooting)
## Optional
- [Website](https://liambx.com/)
- [Open Source](https://github.com/liam-hq/liam)
What is llms.txt?
llms.txt is an open standard that helps AI language models understand your website. By placing a structured markdown file at /llms.txt, websites provide AI search engines like ChatGPT, Claude, and Perplexity with a clear map of their content, services, and documentation. Companies like Liam ERD use it to ensure AI accurately represents their brand when answering user queries. Read the spec.
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