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Evm Token Decimals

Community
Fmarzochi
evm-token-decimals

Prevent silent decimal mismatch bugs across EVM chains. Covers runtime decimal lookup, chain-aware caching, bridged-token precision drift, and safe normalization for bots, dashboards, and DeFi tools.

Overview

PublisherFmarzochi
RepositoryEGC
Skill nameevm-token-decimals
Stars
68
Forks
49
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Fmarzochi on GitHub. Read the source before you install it.

Installation

Install the Evm Token Decimals AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Fmarzochi/EGC/tree/main/skills/backend/evm-token-decimals
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Fmarzochi/EGC.git /tmp/EGC
mkdir -p .claude/skills
cp -r /tmp/EGC/skills/backend/evm-token-decimals .claude/skills/evm-token-decimals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evm Token Decimals in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evm Token Decimals on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evm Token Decimals is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

EVM Token Decimals

Silent decimal mismatches are one of the easiest ways to ship balances or USD values that are off by orders of magnitude without throwing an error.

When to Use

  • Reading ERC-20 balances in Python, TypeScript, or Solidity
  • Calculating fiat values from on-chain balances
  • Comparing token amounts across multiple EVM chains
  • Handling bridged assets
  • Building portfolio trackers, bots, or aggregators

How It Works

Never assume stablecoins use the same decimals everywhere. Query decimals() at runtime, cache by (chain_id, token_address), and use decimal-safe math for value calculations.

Examples

Query decimals at runtime

python
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)

Do not hardcode 1_000_000 because a symbol usually has 6 decimals somewhere else.

Cache by chain and token

python
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()

Handle odd tokens defensively

python
try:
    decimals = contract.functions.decimals().call()
except Exception:
    logging.warning(
        "decimals() reverted on %s (chain %s), defaulting to 18",
        token_address,
        chain_id,
    )
    decimals = 18

Log the fallback and keep it visible. Old or non-standard tokens still exist.

Normalize to 18-decimal WAD in Solidity

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 with ethers

typescript
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);
}

Quick on-chain check

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

Rules

  • Always query decimals() at runtime
  • Cache by chain plus token address, not symbol
  • Use Decimal, BigInt, or equivalent exact math, not float
  • Re-query decimals after bridging or wrapper changes
  • Normalize internal accounting consistently before comparison or pricing

Frequently asked questions

What does the Evm Token Decimals AI skill do?

Prevent silent decimal mismatch bugs across EVM chains. Covers runtime decimal lookup, chain-aware caching, bridged-token precision drift, and safe normalization for bots, dashboards, and DeFi tools.

Why use Evm Token Decimals on TypingMind?

Because you install it once and use it with any model. Evm Token Decimals is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evm Token Decimals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Fmarzochi/EGC/tree/main/skills/backend/evm-token-decimals. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evm Token Decimals?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evm Token Decimals?

As many as you like. As long as a model supports skills, you can use Evm Token Decimals with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evm Token Decimals AI skill free?

Yes. It is published on GitHub by Fmarzochi under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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