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TOON (Token-Oriented Object Notation) encoding for LLM-efficient data representation. 30-60% token savings vs JSON for structured data.

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SKILL.md

name toon-format
description TOON (Token-Oriented Object Notation) encoding for LLM-efficient data representation. 30-60% token savings vs JSON for structured data.

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)

name: John
age: 30
active: true

Arrays with Explicit Length

tags[3]: red, green, blue

Tabular Data (Primary Use Case)

# 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:

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:

description: "This is a multi-line\nstring value that preserves\nline breaks."

Nested Objects

config:
  database:
    host: localhost
    port: 5432
  cache:
    enabled: true

Encoding Patterns for Documentation

Index Files

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

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):

{"pages":[{"path":"/intro","section":"guide","title":"Introduction","priority":"core"},{"path":"/setup","section":"guide","title":"Setup","priority":"core"}]}

TOON (~100 tokens):

pages[2]{path,section,title,priority}:
/intro,guide,Introduction,core
/setup,guide,Setup,core

Savings: ~64%

Tools & Libraries

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:

# 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/