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Agent skill

LLM Trading Agent Security

by affaan-m in affaan-m/ECC

Security patterns for autonomous trading agents with wallet or transaction authority.

MITAuto-check passedSecurity

Install LLM Trading Agent Security

skills CLI
$ npx skills add affaan-m/ECC --skill llm-trading-agent-security -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC llm-trading-agent-security --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/llm-trading-agent-security .claude/skills/llm-trading-agent-security && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
llm-trading-agent-security
GitHub stars
277k
Used in
2 other repos
Token cost
~1.2k tokens
SKILL.md length
221 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

Security patterns for autonomous trading agents with wallet or transaction authority.

  • An autonomous agent holds wallet
  • SKILL.md covers When to Use, How It Works, Examples and Pre-Deploy Checklist
  • Reaches rpc.flashbots.net; needs TRADING_WALLET_PRIVATE_KEY
  • Transaction authority and its limits

What it does

LLM Trading Agent Security is an agent skill from affaan-m/ECC. Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling. Use when an autonomous agent holds wallet or transaction authority and its limits, simulation, or key handling need review.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Security, covering Prompt injection and agent security, Trading and backtesting and Autonomous loops. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • An autonomous agent holds wallet
  • Transaction authority and its limits
  • Key handling need review

Example prompts

  • “/llm-trading-agent-security”

Requirements

  • Python 3
  • A credential in TRADING_WALLET_PRIVATE_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • rpc.flashbots.net

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • TRADING_WALLET_PRIVATE_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

LLM Trading Agent Security loads about 1.2k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 221 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 221 words, ~1,158 tokens.

Download SKILL.mdSave it as .claude/skills/llm-trading-agent-security/SKILL.md (or your agent's skills folder).
name
llm-trading-agent-security
description
Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling. Use when an autonomous agent holds wallet or transaction authority and its limits, simulation, or key handling need review.
metadata.version
1.0.0
metadata.origin
ECC direct-port adaptation

LLM Trading Agent Security

Autonomous trading agents have a harsher threat model than normal LLM apps: an injection or bad tool path can turn directly into asset loss.

When to Use

  • Building an AI agent that signs and sends transactions
  • Auditing a trading bot or on-chain execution assistant
  • Designing wallet key management for an agent
  • Giving an LLM access to order placement, swaps, or treasury operations

How It Works

Layer the defenses. No single check is enough. Treat prompt hygiene, spend policy, simulation, execution limits, and wallet isolation as independent controls.

Examples

Treat prompt injection as a financial attack
python
import re

INJECTION_PATTERNS = [
    r'ignore (previous|all) instructions',
    r'new (task|directive|instruction)',
    r'system prompt',
    r'send .{0,50} to 0x[0-9a-fA-F]{40}',
    r'transfer .{0,50} to',
    r'approve .{0,50} for',
]

def sanitize_onchain_data(text: str) -> str:
    for pattern in INJECTION_PATTERNS:
        if re.search(pattern, text, re.IGNORECASE):
            raise ValueError(f"Potential prompt injection: {text[:100]}")
    return text

Do not blindly inject token names, pair labels, webhooks, or social feeds into an execution-capable prompt.

Hard spend limits
python
from decimal import Decimal

MAX_SINGLE_TX_USD = Decimal("500")
MAX_DAILY_SPEND_USD = Decimal("2000")

class SpendLimitError(Exception):
    pass

class SpendLimitGuard:
    def check_and_record(self, usd_amount: Decimal) -> None:
        if usd_amount > MAX_SINGLE_TX_USD:
            raise SpendLimitError(f"Single tx ${usd_amount} exceeds max ${MAX_SINGLE_TX_USD}")

        daily = self._get_24h_spend()
        if daily + usd_amount > MAX_DAILY_SPEND_USD:
            raise SpendLimitError(f"Daily limit: ${daily} + ${usd_amount} > ${MAX_DAILY_SPEND_USD}")

        self._record_spend(usd_amount)
Simulate before sending
python
class SlippageError(Exception):
    pass

async def safe_execute(self, tx: dict, expected_min_out: int | None = None) -> str:
    sim_result = await self.w3.eth.call(tx)

    if expected_min_out is None:
        raise ValueError("min_amount_out is required before send")

    actual_out = decode_uint256(sim_result)
    if actual_out < expected_min_out:
        raise SlippageError(f"Simulation: {actual_out} < {expected_min_out}")

    signed = self.account.sign_transaction(tx)
    return await self.w3.eth.send_raw_transaction(signed.raw_transaction)
Circuit breaker
python
class TradingCircuitBreaker:
    MAX_CONSECUTIVE_LOSSES = 3
    MAX_HOURLY_LOSS_PCT = 0.05

    def check(self, portfolio_value: float) -> None:
        if self.consecutive_losses >= self.MAX_CONSECUTIVE_LOSSES:
            self.halt("Too many consecutive losses")

        if self.hour_start_value <= 0:
            self.halt("Invalid hour_start_value")
            return

        hourly_pnl = (portfolio_value - self.hour_start_value) / self.hour_start_value
        if hourly_pnl < -self.MAX_HOURLY_LOSS_PCT:
            self.halt(f"Hourly PnL {hourly_pnl:.1%} below threshold")
Wallet isolation
python
import os
from eth_account import Account

private_key = os.environ.get("TRADING_WALLET_PRIVATE_KEY")
if not private_key:
    raise EnvironmentError("TRADING_WALLET_PRIVATE_KEY not set")

account = Account.from_key(private_key)

Use a dedicated hot wallet with only the required session funds. Never point the agent at a primary treasury wallet.

MEV and deadline protection
python
import time

PRIVATE_RPC = "https://rpc.flashbots.net"
MAX_SLIPPAGE_BPS = {"stable": 10, "volatile": 50}
deadline = int(time.time()) + 60

Pre-Deploy Checklist

  • External data is sanitized before entering the LLM context
  • Spend limits are enforced independently from model output
  • Transactions are simulated before send
  • min_amount_out is mandatory
  • Circuit breakers halt on drawdown or invalid state
  • Keys come from env or a secret manager, never code or logs
  • Private mempool or protected routing is used when appropriate
  • Slippage and deadlines are set per strategy
  • All agent decisions are audit-logged, not just successful sends

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/llm-trading-agent-security of affaan-m/ECC.

Open the folder on GitHubat commit 2d515e4

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

LLM Trading Agent Security next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

LLM Trading Agent Security compared with similar skills
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LLM Trading Agent Security this skillaffaan-m/ECC277k2 repos~1.2kAutomated safety check: PassMIT
Skill Scannergetsentry/skills1k4 repos~2.5kAutomated safety check: WarnApache-2.0
Forensifyalexgreensh/repo-forensics190—~2.5kAutomated safety check: NotesCustom licence
Hol Guardhashgraph-online/hol-guard845—~542Automated safety check: PassApache-2.0
Kesekit Checkcdppcorp/KESE-KIT360—~1.3kAutomated safety check: PassMIT
Setuphashgraph-online/hol-guard845—~443Automated safety check: PassApache-2.0

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Categories

Questions about LLM Trading Agent Security

What does LLM Trading Agent Security do?

Security patterns for autonomous trading agents with wallet or transaction authority. LLM Trading Agent Security is an agent skill from affaan-m/ECC. Security patterns for autonomous trading agents with wallet or transaction authority.

When should I use LLM Trading Agent Security?

LLM Trading Agent Security fits situations like: an autonomous agent holds wallet; transaction authority and its limits; key handling need review.

How do I install LLM Trading Agent Security in Claude Code?

Run `npx skills add affaan-m/ECC --skill llm-trading-agent-security -a claude-code`. Or copy the skill folder (skills/llm-trading-agent-security in affaan-m/ECC) into .claude/skills/llm-trading-agent-security in your project. Claude Code loads it when a task matches its description.

How do I install LLM Trading Agent Security in Codex?

Run `npx skills add affaan-m/ECC --skill llm-trading-agent-security -a codex`. Or copy the skill folder (skills/llm-trading-agent-security in affaan-m/ECC) into .agents/skills/llm-trading-agent-security in your project. Codex loads it when a task matches its description.

Can I use LLM Trading Agent Security in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill llm-trading-agent-security -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-trading-agent-security, .gemini/skills/llm-trading-agent-security, .github/skills/llm-trading-agent-security and .opencode/skills/llm-trading-agent-security in your project.

What does LLM Trading Agent Security need to run?

Going by SKILL.md and its folder, LLM Trading Agent Security needs credentials named TRADING_WALLET_PRIVATE_KEY. Our summary lists: Python 3; A credential in TRADING_WALLET_PRIVATE_KEY.

Does LLM Trading Agent Security access the network?

SKILL.md names 1 domain. In commands or code: rpc.flashbots.net; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is LLM Trading Agent Security safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does LLM Trading Agent Security use?

LLM Trading Agent Security is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does LLM Trading Agent Security use?

About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to LLM Trading Agent Security?

Skills that share tags, products or a category with LLM Trading Agent Security: Skill Scanner (getsentry/skills, 1k stars), Forensify (alexgreensh/repo-forensics, 190 stars), Hol Guard (hashgraph-online/hol-guard, 845 stars) and Kesekit Check (cdppcorp/KESE-KIT, 360 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LLM Trading Agent Security?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.