toon-format
TOON (Token-Oriented Object Notation) encoding for LLM-efficient data representation. 30-60% token savings vs JSON for structured data.
Best use case
toon-format is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
TOON (Token-Oriented Object Notation) encoding for LLM-efficient data representation. 30-60% token savings vs JSON for structured data.
Teams using toon-format should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/toon-format/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How toon-format Compares
| Feature / Agent | toon-format | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
TOON (Token-Oriented Object Notation) encoding for LLM-efficient data representation. 30-60% token savings vs JSON for structured data.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# TOON Format Skill
TOON is a compact, human-readable encoding of JSON designed for LLM prompts.
## Variables
| Variable | Default | Description |
|----------|---------|-------------|
| VALIDATION_MODE | strict | `strict` (CLI validation required) or `lenient` (parser permissive) |
| AUTO_FIX | true | Run fix scripts automatically on validation failure |
## Instructions
**MANDATORY** - Follow the format rules in this skill and `reference/format-rules.md`.
- Always include explicit `[count]` for arrays
- Use 2-space indentation for tabular rows
- Add blank line after tabular arrays to terminate them
- Use semicolons for nested arrays in cells (NOT commas)
## Red Flags - STOP and Reconsider
If you're about to:
- Put commas inside quoted strings in tabular cells
- Omit the `[count]` from array declarations
- Forget the blank line after a tabular array
- Use YAML-style `- item` list syntax
- Add comments with `#`
**STOP** -> Check `reference/format-rules.md` -> Then proceed
## Workflow
1. [ ] Determine if TOON is appropriate (see "When to Use TOON" below)
2. [ ] Design data structure for tabular representation
3. [ ] **CHECKPOINT**: Verify format matches rules
4. [ ] Write TOON with proper indentation
5. [ ] Validate with `npx @toon-format/cli --decode`
6. [ ] If errors, run auto-fix scripts from `reference/validation-tools.md`
## Cookbook
### Format Rules
- IF: Writing or editing TOON files
- THEN: Read `reference/format-rules.md`
- COVERS: Syntax rules, constraints, correct patterns
### Validation & Fixing
- IF: TOON file fails validation
- THEN: Read `reference/validation-tools.md`
- COVERS: CLI validation, auto-fix scripts
## When to Use TOON
**Good candidates:**
- Uniform tabular data (lists of similar objects)
- Documentation indexes and manifests
- Repeated key-value structures
- Any structured data being fed to LLMs
**Use JSON instead for:**
- Deeply nested structures
- Non-uniform data (mixed schemas)
- Data requiring frequent programmatic manipulation
- Configuration files read by tools
## TOON Syntax
### Key-Value (YAML-like)
```toon
name: John
age: 30
active: true
```
### Arrays with Explicit Length
```toon
tags[3]: red, green, blue
```
### Tabular Data (Primary Use Case)
```toon
# Syntax: name[count]{col1,col2,col3}:
# Followed by 2-space indented CSV-like rows
users[3]{id,name,email}:
1,John,john@example.com
2,Jane,jane@example.com
3,Bob,bob@example.com
```
**IMPORTANT**: Rows MUST be indented with 2 spaces. Always add a blank line after the last row.
### Nested Arrays in Cells
Use semicolons or quote values containing multiple items:
```toon
pages[2]{path,keywords,priority}:
/intro,"overview;basics;start",core
/api,"reference;methods;types",core
```
### Multi-line Strings
Use quoted strings with `\n` for line breaks:
```toon
description: "This is a multi-line\nstring value that preserves\nline breaks."
```
### Nested Objects
```toon
config:
database:
host: localhost
port: 5432
cache:
enabled: true
```
## Encoding Patterns for Documentation
### Index Files
```toon
meta:
category: libraries
generated: 2025-01-15T10:30:00Z
count: 5
items[5]{id,name,description,path,keywords}:
baml,BAML,Structured LLM outputs,ai-docs/libraries/baml/_index.toon,"llm;types;structured"
mcp,MCP,Tool integration protocol,ai-docs/libraries/mcp/_index.toon,"tools;context;servers"
prisma,Prisma,Type-safe database ORM,ai-docs/libraries/prisma/_index.toon,"database;orm;sql"
```
### Page Summaries
```toon
meta:
library: baml
page: error-handling
source_url: https://docs.boundaryml.com/guide/error-handling
content_hash: a3f2c1d4e5
summary:
purpose: "Configure retry policies and fallback strategies for resilient LLM calls."
key_concepts[4]: RetryPolicy,FallbackClient,timeout,exponential backoff
gotchas[2]: Default timeout 60s may be too short,Retries count against rate limits
code_patterns[1]:
- lang: baml
desc: Retry policy configuration
code: "retry_policy Resilient {\n max_retries 3\n strategy exponential\n}"
```
## Token Comparison
**JSON** (~280 tokens):
```json
{"pages":[{"path":"/intro","section":"guide","title":"Introduction","priority":"core"},{"path":"/setup","section":"guide","title":"Setup","priority":"core"}]}
```
**TOON** (~100 tokens):
```toon
pages[2]{path,section,title,priority}:
/intro,guide,Introduction,core
/setup,guide,Setup,core
```
**Savings: ~64%**
## Tools & Libraries
- **TypeScript/JS**: `@toon-format/toon` (npm)
- **Python**: `python-toon` (pip)
- **Online**: https://toontools.vercel.app/playground
- **Spec**: https://toonformat.dev/
## Best Practices
1. Design data structures for tabular representation when possible
2. Use explicit `[count]` for arrays - helps LLM parsing
3. Keep JSON as canonical source, TOON for LLM transport
4. Use semicolons for nested arrays (e.g., `"val1;val2;val3"`)
5. Truncate long strings (code snippets < 200 chars)
6. Always add a blank line after tabular arrays to terminate them
## Format Constraints
**Enforced by official `@toon-format/cli` (use `--decode` to validate):**
| Rule | ❌ Incorrect | ✅ Correct |
|------|-------------|-----------|
| No comments | `# comment` | (remove comments entirely) |
| No YAML-style lists | `- item` | `key[N]{col}:` + indented rows |
| Rows must be indented | `row,data` | ` row,data` (2 spaces) |
| Arrays need count+cols | `items{a,b}:` | `items[3]{a,b}:` |
| No multiline strings | `key: \|` | `key: "line1\nline2"` |
| No pipe delimiters | `val\|val` | `val,val` or `"val;val"` |
| No commas in tabular cells | `"a,b"` | `"a;b"` |
| Blank line AFTER arrays | missing terminator | blank line after last row |
**Validation tools:**
```bash
# Validate with official CLI (use --decode, not validate!)
npx @toon-format/cli --decode file.toon > /dev/null
# Auto-fix common issues (run in this order)
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_comments.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_yaml_lists.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_nested_lists.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_commas.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_pipes.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_multiline.py path/
python3 .claude/ai-dev-kit/dev-tools/scripts/fix_toon_blank_lines.py path/
```Related Skills
file-format-converter
File Format Converter - Auto-activating skill for Data Pipelines. Triggers on: file format converter, file format converter Part of the Data Pipelines skill category.
commit-message-formatter
Commit Message Formatter - Auto-activating skill for DevOps Basics. Triggers on: commit message formatter, commit message formatter Part of the DevOps Basics skill category.
cloudformation-template-creator
Cloudformation Template Creator - Auto-activating skill for AWS Skills. Triggers on: cloudformation template creator, cloudformation template creator Part of the AWS Skills skill category.
cloudformation
AWS CloudFormation infrastructure as code for stack management. Use when writing templates, deploying stacks, managing drift, troubleshooting deployments, or organizing infrastructure with nested stacks.
simple-formatter
Formats text according to specified style guidelines. A clean example skill with no security issues.
dbt-transformation-patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
cloudformation-best-practices
CloudFormation template optimization, nested stacks, drift detection, and production-ready patterns. Use when writing or reviewing CF templates.
baoyu-format-markdown
Formats plain text or markdown files with frontmatter, titles, summaries, headings, bold, lists, and code blocks. Use when user asks to "format markdown", "beautify article", "add formatting", or improve article layout. Outputs to {filename}-formatted.md.
enact-json-formatter
Formats and prettifies JSON with configurable indentation
enact-formatter
Formats and prettifies JSON with configurable indentation
assume-cloudformation-role
Assume AWS IAM role for CloudFormation operations and set temporary credentials as environment variables. Use when working with CloudFormation stacks or when authentication setup is needed before AWS CloudFormation operations.
code-formatter
Automatically format code across multiple languages with opinionated configurations.