llm-trading-agent-security

Overview of llm-trading-agent-security skill

llm-trading-agent-security is a practical security skill for autonomous trading agents that can sign, swap, approve, or send funds. It helps you decide where an LLM-driven trading system can fail, what controls to layer in, and how to reduce the chance that a bad prompt, compromised feed, or unsafe tool call becomes real financial loss.

Who this skill is for

Use the llm-trading-agent-security skill if you are building or reviewing an agent with wallet authority, order placement, treasury access, or on-chain execution. It is especially relevant for teams doing a Security Audit of trading bots, execution assistants, or agentic DeFi workflows.

What problem it solves

The main job is not “make the agent smarter.” It is “make the agent safer to let act.” The skill focuses on prompt injection, spend ceilings, pre-send simulation, circuit breakers, MEV-aware execution, and key isolation so you can separate reasoning from authority.

Why it is different

This is not a generic LLM safety prompt. The llm-trading-agent-security skill treats prompt injection as a financial attack path and emphasizes layered controls over single safeguards. That makes it useful when ordinary prompt engineering is not enough and you need concrete guardrails before deployment.

How to Use llm-trading-agent-security skill

Install and open the source files

Install the llm-trading-agent-security skill with:

npx skills add affaan-m/everything-claude-code --skill llm-trading-agent-security

Then read SKILL.md first. In this repository, there are no supporting rules/, resources/, or scripts/ folders, so the skill body is the primary source of truth. That makes the initial read important: it tells you the intended threat model and the controls the skill expects you to apply.

Turn a rough goal into a usable prompt

The llm-trading-agent-security usage works best when you give it a concrete operating context, not a vague “secure my agent” request. Strong inputs include:

• chain or venue, such as EVM swaps, Solana routing, or cross-chain execution
• what the agent can do, such as approve, swap, bridge, or withdraw
• maximum allowed loss per action or per day
• what data the agent reads, such as social feeds, token metadata, or pricing APIs
• whether the goal is design review, prompt hardening, or production guardrail setup

Example prompt shape:
“Use llm-trading-agent-security to review an agent that reads social posts, proposes trades, and can submit EVM swaps. Identify prompt-injection paths, add spend limits, define simulation checks, and suggest wallet isolation and circuit breaker rules.”

Apply the layered workflow

The skill is most useful when you treat controls as independent layers:

1. sanitize or constrain untrusted text before it reaches the agent
2. limit spend, approval scope, and time window
3. simulate or preview transactions before sending
4. add circuit breakers for abnormal loss or behavior
5. isolate keys and execution privileges from the reasoning model

For llm-trading-agent-security install and usage, this layered approach matters more than any single code snippet. If one layer fails, the others still reduce blast radius.

Read for decisions, not decoration

When you review the repository content, focus on the sections that change implementation decisions:

• When to Use for fit and boundaries
• How It Works for the control stack
• Examples for practical anti-injection and spend-control patterns

If your current agent design cannot support simulation, spend caps, or key separation, that is a sign to redesign before integrating the skill.

llm-trading-agent-security skill FAQ

Is this skill only for DeFi bots?

No. The llm-trading-agent-security skill also fits any agent that can place trades, move assets, or trigger financial actions. If the LLM can change balances, open positions, or approve spending, the threat model applies.

Is it better than a normal security prompt?

Yes, when the system has real execution authority. A normal prompt may remind the model to be careful, but this skill is oriented around concrete controls: injection handling, limits, simulation, and execution boundaries. That makes it more useful for a Security Audit than a generic checklist.

Can beginners use it?

Yes, if they can describe their agent’s actions clearly. Beginners usually get the best results by starting with one narrow workflow, such as “trade suggestions only” or “swap execution with a capped budget,” then expanding after the first review.

When should I not use it?

Do not use llm-trading-agent-security as a substitute for general application security, exchange compliance, or chain-specific audit work. If the agent has no authority to move value, the skill may be more than you need. If it has broad authority, you need this kind of control-focused guidance.

How to Improve llm-trading-agent-security skill

Give the skill the actual trust boundaries

The strongest llm-trading-agent-security results come from naming exactly what the agent can and cannot do. Include allowed actions, blocked actions, approval flow, key custody model, and whether humans must confirm high-risk transactions. Without those boundaries, the output can stay too abstract.

Provide failure cases, not just goals

If you want a useful security review, include likely abuse paths: malicious token metadata, social-post prompt injection, poisoned API responses, stale price data, or oversized approvals. This lets the skill focus on the controls that matter instead of repeating obvious best practices.

Ask for implementation tradeoffs

To improve the llm-trading-agent-security guide output, ask for tradeoffs between safety and automation. For example, request a comparison of strict pre-send simulation versus faster execution, or wallet isolation versus operational convenience. That helps you decide what to ship first.

Iterate after the first pass

After the first answer, tighten the prompt with your real constraints: max order size, latency tolerance, supported chains, and whether you can reject suspicious inputs outright. Then ask the skill to re-rank controls by risk reduction. This usually produces a more actionable Security Audit plan than a broad one-shot request.