LLMs.txt

LLMs.txt#

ChartBook provides an llms.txt file following the llmstxt.org specification to help Large Language Models (LLMs) understand and work with your ChartBook projects.

What is llms.txt?#

The llms.txt file is a proposed standard for providing LLM-friendly documentation. It uses Markdown format with a specific structure designed to help AI assistants quickly understand a project’s purpose, structure, and usage.

File Locations#

ChartBook provides llms.txt files in multiple locations:

File

Description

/llms.txt

Main file at project root

/llm/llms.txt

Copy in dedicated directory

/llm/llms-full.txt

Comprehensive version with all documentation inline

Using llms.txt with AI Assistants#

When working with an AI assistant (like Claude, ChatGPT, or Copilot), you can provide the llms.txt content to give the AI context about ChartBook:

Quick Context#

# Copy the concise version
cat llms.txt

Full Context#

# Copy the comprehensive version with all documentation
cat llm/llms-full.txt

In a Chat Session#

Simply paste the contents of llms.txt or llm/llms-full.txt at the beginning of your conversation with an AI assistant to provide it with context about how to work with ChartBook projects.

Claude Code Skills#

For Claude Code users, ChartBook provides a ready-to-use skill in .claude/skills/chartbook/. Copy this directory to your personal skills folder to enable Claude to automatically understand ChartBook projects.

See the Claude Code Skills documentation for more information on using skills.

llms.txt Content#

Here is the content of the main llms.txt file:

# ChartBook

> A developer platform for data science teams to discover, document, and share analytics work. Provides a centralized catalog for pipelines, charts, and dataframes with automatic documentation website generation.

ChartBook organizes analytics work into **pipelines** (self-contained projects) or **catalogs** (multi-pipeline collections). All configuration uses TOML format (`chartbook.toml`).

## Quick Start

- [Installation](docs_src/getting-started.md): `pip install chartbook` for data loading, `pipx install chartbook` for CLI
- [CLI Reference](docs_src/cli-reference.md): `chartbook build`, `chartbook publish`, `chartbook create-data-glimpses`

## Core Concepts

- [Pipelines](docs_src/user-guide/pipelines.md): Self-contained analytics projects with charts, dataframes, and documentation
- [Charts](docs_src/user-guide/charts.md): Interactive HTML visualizations with metadata and governance tracking
- [Dataframes](docs_src/user-guide/dataframes.md): Data sources in Parquet format with licensing and lineage tracking
- [Catalog Projects](docs_src/user-guide/catalog-projects.md): Aggregate multiple pipelines into unified documentation

## Configuration

- [Configuration Guide](docs_src/configuration.md): Complete `chartbook.toml` reference with all available fields

## API

- [Python API](docs_src/api/chartbook.md): `from chartbook import data; df = data.load(pipeline="ID", dataframe="df_id")`

## Key Commands

chartbook build           # Generate HTML documentation website
chartbook build -f        # Force overwrite existing docs
chartbook publish            # Publish to directory
chartbook create-data-glimpses  # Create data summary report
chartbook config             # Configure default catalog path

## Minimal Configuration Example

[config]
type = "pipeline"
chartbook_format_version = "0.0.6"

[site]
title = "My Analytics"
author = "Your Name"
copyright = "2025"

[pipeline]
id = "MYPROJ"
pipeline_name = "My Pipeline"
pipeline_description = "Analytics pipeline description"
lead_pipeline_developer = "Your Name"

[charts.my_chart]
chart_name = "My Chart"
short_description_chart = "Chart description"
dataframe_id = "my_data"
path_to_html_chart = "./_output/chart.html"
chart_docs_path = "./docs_src/charts/chart.md"

[dataframes.my_data]
dataframe_name = "My Data"
short_description_df = "Data description"
data_sources = ["Source"]
path_to_parquet_data = "./_data/data.parquet"
date_col = "date"

## Directory Structure

my-pipeline/
├── chartbook.toml      # Configuration
├── _data/               # Parquet data files
├── _output/             # Generated HTML charts
├── docs_src/            # Markdown documentation
│   ├── charts/
│   └── dataframes/
└── src/                 # Python source code

Resources#

Contents