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Baixar Zip Baixando... Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name toon-format description Expert skill for Token-Oriented Object Notation (TOON) — compact, schema-aware JSON encoding for LLM prompts that reduces tokens by ~40%. triggers ["convert JSON to TOON format","reduce LLM prompt tokens","encode data for LLM input","use TOON for AI prompts","serialize data with fewer tokens","TOON format encoding and decoding","token-efficient data format for language models","replace JSON with TOON in my prompt"]
Token-Oriented Object Notation (TOON)
Skill by ara.so — Daily 2026 Skills collection.
TOON is a compact, human-readable encoding of the JSON data model that minimizes tokens for LLM input. It combines YAML-style indentation for nested objects with CSV-style tabular layout for uniform arrays, achieving ~40% token reduction while maintaining or improving LLM comprehension accuracy.
Installation
npm install @toon-format/toon
pnpm add @toon-format/toon
yarn add @toon-format/toon
CLI
npm install -g @toon-format/toon
toon encode input.json
toon encode input.json -o output.toon
toon decode input.toon
toon decode input.toon -o output.json
cat data.json | toon encode
cat data.toon | toon decode
toon decode input.toon --pretty
Core API
encode / stringify import { encode, decode } from '@toon-format/toon' ;
const data = {
context : {
task : 'Our favorite hikes together' ,
location : 'Boulder' ,
season : 'spring_2025' ,
},
friends : ['ana' , 'luis' , 'sam' ],
hikes : [
{ id : 1 , name : 'Blue Lake Trail' , distanceKm : 7.5 , elevationGain : 320 , companion : 'ana' , wasSunny : true },
{ id : 2 , name : 'Ridge Overlook' , distanceKm : 9.2 , elevationGain : 540 , companion : 'luis' , wasSunny : false },
{ id : 3 , name : 'Wildflower Loop' , distanceKm : 5.1 , elevationGain : 180 , companion : 'sam' , wasSunny : true },
],
};
const toon = encode (data);
console .log (toon);
decode / parse import { decode } from '@toon-format/toon' ;
const toonString = `
context:
task: Our favorite hikes together
location: Boulder
friends[2]: ana,luis
hikes[2]{id,name,distanceKm}:
1,Blue Lake Trail,7.5
2,Ridge Overlook,9.2
` ;
const parsed = decode (toonString);
console .log (parsed.hikes [0 ].name );
Encoding options import { encode } from '@toon-format/toon' ;
const toon = encode (data, {
tabular : 'always' ,
indent : 2 ,
quoting : 'auto' ,
});
Format Overview
Primitive scalars TOON encodes scalars the same way as YAML — unquoted when unambiguous:
name: Alice
age: 30
active: true
score: 98.6
nothing: null
Nested objects (YAML-style indentation) user:
name: Alice
address:
city: Boulder
zip: 80301
Flat arrays (scalar items) Square brackets declare the array length, values are comma-separated:
tags[3]: typescript,llm,serialization
scores[4]: 10,20,30,40
Uniform object arrays (tabular format) Curly braces declare the field headers; each subsequent indented line is a row:
employees[3]{id,name,department,salary}:
1,Alice,Engineering,95000
2,Bob,Marketing,72000
3,Carol,Engineering,102000
Quoting rules Values containing commas, colons, or newlines are quoted:
notes[2]: "hello, world","line1\nline2"
messages[1]{from,text}:
alice,"See you at 3:00, okay?"
Mixed nesting company:
name: Acme Corp
founded: 1987
offices[2]: NYC,SF
teams[2]{name,headcount}:
Engineering,45
Marketing,20
Using TOON with LLMs
Direct prompt injection import { encode } from '@toon-format/toon' ;
import OpenAI from 'openai' ;
const client = new OpenAI ({ apiKey : process.env .OPENAI_API_KEY });
async function queryWithToon (data : unknown , question : string const toon = encode (data);
const response = await client.chat .completions .create ({
model : 'gpt-4o-mini' ,
messages : [
{
role : 'system' ,
content : [
'You are a data analyst. The user will provide data in TOON format.' ,
'TOON is a compact encoding of JSON: indentation = nesting,' ,
'key[N]: v1,v2 = array of N scalars,' ,
'key[N]{f1,f2}: rows = array of N objects with fields f1, f2.' ,
].join (' ' ),
},
{
role : 'user' ,
content : `Data:\n\`\`\`\n${toon} \n\`\`\`\n\nQuestion: ${question} ` ,
},
],
});
return response.choices [0 ].message .content ;
}
const employees = [
{ id : 1 , name : 'Alice' , dept : 'Eng' , salary : 95000 },
{ id : 2 , name : 'Bob' , dept : 'Marketing' , salary : 72000 },
];
const answer = await queryWithToon (
{ employees },
'Who has the highest salary?'
);
Anthropic / Claude import { encode } from '@toon-format/toon' ;
import Anthropic from '@anthropic-ai/sdk' ;
const client = new Anthropic ({ apiKey : process.env .ANTHROPIC_API_KEY });
async function analyzeWithClaude (data : unknown , prompt : string const toon = encode (data);
const message = await client.messages .create ({
model : 'claude-haiku-4-5-20251001' ,
max_tokens : 1024 ,
system :
'Data is in TOON format: indented = nested objects, key[N]: vals = scalar array, key[N]{fields}: rows = object array.' ,
messages : [
{
role : 'user' ,
content : `\`\`\`toon\n${toon} \n\`\`\`\n\n${prompt} ` ,
},
],
});
return message.content [0 ].type === 'text' ? message.content [0 ].text : null ;
}
Token count comparison utility import { encode } from '@toon-format/toon' ;
import { encode as gptEncode } from 'gpt-tokenizer' ;
function compareTokens (data : unknown const jsonStr = JSON .stringify (data);
const toonStr = encode (data);
const jsonTokens = gptEncode (jsonStr).length ;
const toonTokens = gptEncode (toonStr).length ;
const savings = (((jsonTokens - toonTokens) / jsonTokens) * 100 ).toFixed (1 );
console .log (`JSON: ${jsonTokens} tokens` );
console .log (`TOON: ${toonTokens} tokens` );
console .log (`Saved: ${savings} %` );
return { jsonTokens, toonTokens, savings : parseFloat (savings) };
}
Common Patterns
Batch API calls with TOON import { encode } from '@toon-format/toon' ;
function encodeRecords<T>(records : T[]): string [] {
return records.map ((r ) => encode (r));
}
function encodeAll<T>(records : T[], key = 'records' ): string {
return encode ({ [key]: records });
}
RAG / retrieval context injection import { encode } from '@toon-format/toon' ;
interface SearchResult {
id : string ;
title : string ;
snippet : string ;
score : number ;
url : string ;
}
function buildRagContext (results : SearchResult []string {
return encode ({ results });
}
Streaming encode for large datasets import { encode } from '@toon-format/toon' ;
import { createReadStream, createWriteStream } from 'fs' ;
async function convertFile (inputPath : string , outputPath : string const raw = await fs.promises .readFile (inputPath, 'utf-8' );
const data = JSON .parse (raw);
const toon = encode (data);
await fs.promises .writeFile (outputPath, toon, 'utf-8' );
const jsonBytes = Buffer .byteLength (raw);
const toonBytes = Buffer .byteLength (toon);
console .log (`Reduced size by ${(((jsonBytes - toonBytes) / jsonBytes) * 100 ).toFixed(1 )} %` );
}
Schema-aware encoding (TypeScript) import { encode, decode } from '@toon-format/toon' ;
interface Employee {
id : number ;
name : string ;
department : string ;
salary : number ;
active : boolean ;
}
interface EmployeeReport {
generatedAt : string ;
employees : Employee [];
}
const report : EmployeeReport = {
generatedAt : new Date ().toISOString (),
employees : [
{ id : 1 , name : 'Alice' , department : 'Engineering' , salary : 95000 , active : true },
{ id : 2 , name : 'Bob' , department : 'Marketing' , salary : 72000 , active : true },
],
};
const toon = encode (report);
const recovered = decode (toon) as EmployeeReport ;
console .log (recovered.employees [0 ].name );
Express middleware for TOON content-type import express from 'express' ;
import { encode, decode } from '@toon-format/toon' ;
const app = express ();
app.use ((req, res, next ) => {
if (req.headers ['content-type' ]?.startsWith ('text/toon' )) {
let body = '' ;
req.on ('data' , (chunk ) => (body += chunk));
req.on ('end' , () => {
try {
(req as any ).toonBody = decode (body);
next ();
} catch (e) {
res.status (400 ).json ({ error : 'Invalid TOON body' });
}
});
} else {
next ();
}
});
app.get ('/api/employees' , (req, res ) => {
const employees = [
{ id : 1 , name : 'Alice' , dept : 'Eng' },
{ id : 2 , name : 'Bob' , dept : 'Marketing' },
];
if (req.headers .accept ?.includes ('text/toon' )) {
res.setHeader ('Content-Type' , 'text/toon; charset=utf-8' );
res.send (encode ({ employees }));
} else {
res.json ({ employees });
}
});
When to Use TOON vs JSON Scenario Recommendation Uniform arrays of objects ✅ TOON (biggest savings) Deeply nested / non-uniform ⚠️ Benchmark both; JSON-compact may win Pure flat tabular data Consider CSV (smaller) or TOON (structured) Latency-critical (local models) Benchmark TTFT + tokens/sec Programmatic API calls Keep JSON; encode to TOON only for LLM input Semi-uniform (~40–60% tabular) Benchmark; savings diminish
Troubleshooting
Values with commas parse incorrectly Wrap them in double quotes in your TOON string, or ensure encode() handles it automatically:
const data = { tags : ['hello, world' , 'foo,bar' ] };
encode (data);
Round-trip type loss (numbers vs strings) TOON uses unquoted values for numbers and booleans. Ensure your data uses proper JS types before encoding — don't pass "95000" (string) when you mean 95000 (number):
{ salary : 95000 , active : true }
{ salary : '95000' , active : 'true' }
LLM misreads tabular rows Add a brief TOON format explanation to your system prompt:
TOON format rules:
- Indentation = nested object
- key[N]: v1,v2,v3 = array of N scalar values
- key[N]{field1,field2}: followed by N indented rows = array of objects
CLI not found after global install
npm bin -g
npx @toon-format/toon encode input.json
Decoding fails on hand-written TOON Common mistakes in hand-written TOON:
Missing length declaration: items{id,name}: → must be items[2]{id,name}:
Inconsistent indentation (mix of tabs/spaces)
Unquoted values containing : as first character
Resources 