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evm-token-decimals

EVM チェーン全体での暗黙的な小数点の不一致バグを防止します。ランタイムでの小数点検索、チェーン対応のキャッシュ、ブリッジトークンの精度ドリフト、ボット、ダッシュボード、DeFiツール向けの安全な正規化をカバーします。EVM チェーン間でトークン量を処理する場合、または残高、価格、送金額が桁違いにずれている場合に使用します。

evm-token-decimals とは?

EVM Token Decimals は、EVM チェーン全体でトークン量を扱う際に、小数点の不一致による注文の誤差が発生するのを防ぐスキルです。このスキルは、ERC-20 トークンの decimal 値を実行時に動的に取得し、`(chain_id, token_address)` をキーとしてキャッシュすることで、シンボルに依存したハードコードを避けます。また、ブリッジトークンの精度ドリフトや非標準トークンへの防御的対応、Solidity での 18 進数 WAD への正規化、TypeScript/ethers や Python/web3.py での実装例を提供します。このスキルは、ポートフォリオトラッカー、ボット、アグリゲーター、ダッシュボードなど、正確なトークン金額計算が必要なあらゆるツールに適用可能で、小数点の誤りによるバグを根本から排除します。

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

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ドキュメント

EVM トークン Decimals

小数点の暗黙的な不一致は、エラーを発生させずに残高や USD 価格が桁違いにずれる原因となる最も簡単な方法のひとつです。

使用するタイミング

  • Python、TypeScript、または Solidity で ERC-20 残高を読み取る場合
  • オンチェーン残高から法定通貨相当額を計算する場合
  • 複数の EVM チェーンにわたってトークン量を比較する場合
  • ブリッジされた資産を扱う場合
  • ポートフォリオトラッカー、ボット、またはアグリゲーターを構築する場合

仕組み

ステーブルコインがどこでも同じ decimals を持つと想定しないでください。実行時に decimals() をクエリし、(chain_id, token_address) でキャッシュし、decimal セーフな計算を使用して値を計算します。

例

実行時に decimals をクエリ

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)

シンボルがどこかで 6 桁の decimals を持つからといって、1_000_000 をハードコードしないでください。

チェーンとトークンでキャッシュ

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()

異常なトークンを防御的に処理

try:
    decimals = contract.functions.decimals().call()
except Exception:
    logging.warning(
        "decimals() が %s (チェーン %s) でリバートしました。デフォルト値 18 を使用します",
        token_address,
        chain_id,
    )
    decimals = 18

フォールバックをログに記録し、可視性を保ってください。古いまたは非標準のトークンは依然として存在します。

Solidity で 18 桁の WAD に正規化

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 (ethers 使用)

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);
}

クイックオンチェーンチェック

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

ルール

  • 必ず実行時に decimals() をクエリする
  • シンボルではなく、チェーンとトークンアドレスでキャッシュする
  • Decimal、BigInt、または同等の正確な計算を使用し、float は使用しない
  • ブリッジやラッパーの変更後は decimals を再クエリする
  • 内部会計を比較や価格設定の前に一貫して正規化する

Individual skills in this repo

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

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

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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