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evm-token-decimals

Evita bugs silenciosos de incompatibilidade de decimais em cadeias EVM. Abrange consulta de decimais em tempo de execução, cache com reconhecimento de cadeia, desvio de precisão de tokens bridgeados e normalização segura para bots, dashboards e ferramentas DeFi. Use ao lidar com quantidades de tokens em várias cadeias EVM ou quando um saldo, preço ou valor de transferência estiver fora por ordens de magnitude.

O que é evm-token-decimals?

Este skill resolve um dos problemas mais comuns e silenciosos no desenvolvimento em EVM: decimais de tokens incorretos. Cada token ERC-20 pode ter um número diferente de decimais (como 6 para USDC ou 18 para ETH), e hardcodar valores como `1_000_000` pode levar a erros de cálculo de ordens de magnitude sem nenhum erro ser gerado. O skill fornece orientação para consultar dinamicamente `decimals()` em tempo de execução usando Web3.py, ethers.js ou cast, e armazenar em cache por `(chain_id, token_address)` para evitar chamadas repetitivas. Ele também aborda a normalização de quantidades para um padrão interno (como WAD de 18 decimais em Solidity) e lida com tokens não padronizados que podem reverter a função `decimals()`. Suporta Python, TypeScript e Solidity, e é ideal para bots de negociação, rastreadores de portfólio, agregadores e qualquer ferramenta DeFi que processe saldos de tokens entre múltiplas cadeias EVM. A abordagem principal é sempre consultar os decimais em tempo de execução e nunca confiar em suposições baseadas no símbolo do token.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/evm-token-decimals

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Documentação

Decimais de Token EVM

Incompatibilidades silenciosas de decimais são uma das maneiras mais fáceis de gerar saldos ou valores em USD que estão errados por ordens de magnitude sem gerar um erro.

Quando Usar

  • Lendo saldos ERC-20 em Python, TypeScript ou Solidity
  • Calculando valores fiduciários a partir de saldos on-chain
  • Comparando quantidades de tokens em múltiplas cadeias EVM
  • Lidando com ativos bridgeados
  • Construindo rastreadores de portfólio, bots ou agregadores

Como Funciona

Nunca presuma que stablecoins usam os mesmos decimais em todos os lugares. Consulte decimals() em tempo de execução, armazene em cache por (chain_id, token_address) e use matemática segura para decimais em cálculos de valor.

Exemplos

Consultar decimais em tempo de execução

from decimal import Decimal
from web3 import Web3

ERC20_ABI = [
    {"name": "decimals", "type": "function", "inputs": [],
     "outputs": [{"type": "uint8"}], "stateMutability": "view"},
    {"name": "balanceOf", "type": "function",
     "inputs": [{"name": "account", "type": "address"}],
     "outputs": [{"type": "uint256"}], "stateMutability": "view"},
]

def get_token_balance(w3: Web3, token_address: str, wallet: str) -> Decimal:
    contract = w3.eth.contract(
        address=Web3.to_checksum_address(token_address),
        abi=ERC20_ABI,
    )
    decimals = contract.functions.decimals().call()
    raw = contract.functions.balanceOf(Web3.to_checksum_address(wallet)).call()
    return Decimal(raw) / Decimal(10 ** decimals)

Não hardcode 1_000_000 só porque um símbolo geralmente tem 6 decimais em outro lugar.

Cache por cadeia e token

from functools import lru_cache

@lru_cache(maxsize=512)
def get_decimals(chain_id: int, token_address: str) -> int:
    w3 = get_web3_for_chain(chain_id)
    contract = w3.eth.contract(
        address=Web3.to_checksum_address(token_address),
        abi=ERC20_ABI,
    )
    return contract.functions.decimals().call()

Lidar defensivamente com tokens incomuns

try:
    decimals = contract.functions.decimals().call()
except Exception:
    logging.warning(
        "decimals() reverteu em %s (cadeia %s), usando 18 como padrão",
        token_address,
        chain_id,
    )
    decimals = 18

Registre o fallback e mantenha-o visível. Tokens antigos ou não padronizados ainda existem.

Normalizar para WAD de 18 decimais em Solidity

interface IERC20Metadata {
    function decimals() external view returns (uint8);
}

function normalizeToWad(address token, uint256 amount) internal view returns (uint256) {
    uint8 d = IERC20Metadata(token).decimals();
    if (d == 18) return amount;
    if (d < 18) return amount * 10 ** (18 - d);
    return amount / 10 ** (d - 18);
}

TypeScript com ethers

import { Contract, formatUnits } from 'ethers';

const ERC20_ABI = [
  'function decimals() view returns (uint8)',
  'function balanceOf(address) view returns (uint256)',
];

async function getBalance(provider: any, tokenAddress: string, wallet: string): Promise<string> {
  const token = new Contract(tokenAddress, ERC20_ABI, provider);
  const [decimals, raw] = await Promise.all([
    token.decimals(),
    token.balanceOf(wallet),
  ]);
  return formatUnits(raw, decimals);
}

Verificação rápida on-chain

cast call <token_address> "decimals()(uint8)" --rpc-url <rpc>

Regras

  • Sempre consulte decimals() em tempo de execução
  • Armazene em cache por cadeia mais endereço do token, não pelo símbolo
  • Use Decimal, BigInt ou matemática exata equivalente, não float
  • Consulte novamente os decimais após bridge ou mudanças de wrapper
  • Normalize a contabilidade interna de forma consistente antes de comparação ou precificação

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

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affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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