AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ufy2024/AuC --skill evm-token-decimalsgit clone --depth 1 https://github.com/ufy2024/AuCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ufy2024/auc/evm-token-decimals)<a href="https://agentmods.dev/skills/ufy2024/auc/evm-token-decimals"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/evm-token-decimals.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 21 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.01030 |
| Opus 5 | $0.00022 | $0.00515 |
| Sonnet 5 | $0.00009 | $0.00206 |
| Haiku 4.5 | $0.00004 | $0.00103 |
Grade A, and why
evm-token-decimals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- evm-token-decimals — 91% identical, 28 lines differ
- evm-token-decimals — 89% identical, 29 lines differ
- evm-token-decimals — 86% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
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
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 151 lines · 44 tokens per session scan A 9ebf0e5ec001
evm-token-decimals is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,030 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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