Risk & Safety Agents¶

Six agents for evaluating risk across counterparties, lending positions, token launches, AI agent interactions, portfolios, and real-world asset investments.

Setup required: For AI Agents — MCP registration and agent installation.

chainaware-counterparty-screener¶

Role: Screens counterparties before executing trades, OTC deals, or B2B crypto transactions.

What it does: Evaluates both fraud risk and on-chain behaviour for a counterparty wallet before a business-to-business transaction is committed. Flags wallets with fraud history, unusual behavioural patterns (wash trading, sandwiching, Sybil indicators), or recent interaction with flagged contracts.

Tools used: predictive_fraud, predictive_behaviour
Model: Claude Haiku 4.5

Example invocation:

@chainaware-counterparty-screener Screen counterparty wallet 0xOTC...DEAL before we execute

Output includes:
- Fraud risk score and classification
- Behavioural profile summary
- Specific counterparty risk flags
- Proceed / Proceed with caution / Decline recommendation

chainaware-lending-risk-assessor¶

Role: Assesses borrower risk for undercollateralised DeFi lending decisions.

What it does: Analyses a borrower wallet's behavioural history (predictive_behaviour) and fraud signals (predictive_fraud) to produce a credit-risk-style assessment. Evaluates repayment likelihood based on past on-chain activity, DeFi engagement depth, and wallet tenure. Designed for protocols offering undercollateralised or reputation-based lending.

Tools used: predictive_behaviour, predictive_fraud
Model: Claude Haiku 4.5

Example invocation:

@chainaware-lending-risk-assessor Assess lending risk for borrower wallet 0xBORROWER...999

Output includes:
- Borrower risk tier (Prime / Standard / Subprime / Decline)
- Behavioural indicators supporting the assessment
- Fraud risk overlay
- Recommended LTV ratio or loan limit
- Conditions or monitoring requirements

chainaware-token-launch-auditor¶

Role: Pre-launch and post-launch token risk audit combining deployer, token, and holder signals.

What it does: Runs a three-signal audit: predictive_rug_pull for the token contract itself, predictive_fraud for the deployer wallet, and predictive_behaviour for the deployer's historical on-chain activity. Produces a structured audit report suitable for community due diligence, launchpad listing decisions, or investor research.

Tools used: predictive_rug_pull, predictive_fraud, predictive_behaviour
Model: Claude Haiku 4.5

Example invocation:

@chainaware-token-launch-auditor Audit this new token launch: contract 0xTOKEN, deployer 0xDEPLOYER

Output includes:
- Token rug pull risk score
- Deployer fraud score
- Deployer behavioural profile (serial launcher? prior exits?)
- Combined launch risk verdict (Safe / Caution / High Risk / Avoid)
- Specific red flags by category
- Recommended next steps for investors

chainaware-agent-screener¶

Role: Screens AI agent wallets before allowing them to interact with a DeFi protocol or treasury.

What it does: As AI agents increasingly transact on-chain autonomously, protocols need to screen agent wallets for fraud patterns and unusual behaviours. This agent evaluates an on-chain AI agent wallet using predictive_fraud and predictive_behaviour, specifically looking for patterns associated with drain attacks, front-running bots, or compromised agent wallets.

Tools used: predictive_fraud, predictive_behaviour
Model: Claude Haiku 4.5

Example invocation:

@chainaware-agent-screener Screen this AI agent wallet before granting treasury access: 0xAGENT...WALLET

Output includes:
- Fraud risk assessment for the agent wallet
- Behavioural anomaly flags (bot-like patterns, MEV activity)
- Agent risk classification
- Allow / Restricted access / Deny recommendation
- Suggested permission scope if allowed

chainaware-portfolio-risk-advisor¶

Role: Portfolio-level risk advisory combining individual token quality with rug pull signals.

What it does: Takes a list of token holdings and evaluates overall portfolio risk by running token_rank_single (quality and rank signals) and predictive_rug_pull (rug pull probability) for each position. Aggregates into a portfolio risk score and identifies which holdings represent the greatest concentration of risk.

Tools used: predictive_rug_pull, token_rank_single
Model: Claude Sonnet 4.6 (complex multi-token synthesis)

Example invocation:

@chainaware-portfolio-risk-advisor Assess my portfolio risk: [0xTOKEN1, 0xTOKEN2, 0xTOKEN3]

Output includes:
- Per-token risk score and rank
- Portfolio overall risk rating
- Highest-risk positions ranked
- Concentration risk warnings
- Diversification recommendations
- Specific tokens to monitor or exit

chainaware-rwa-investor-screener¶

Role: Screens investors in Real-World Asset (RWA) tokenisation platforms for compliance and behaviour.

What it does: RWA platforms serving regulated investment products need to know that investors are not only KYC-compliant but also behave consistently with legitimate investment intent. This agent combines predictive_fraud (compliance risk) with predictive_behaviour (investment behavioural patterns) to screen wallets for RWA platform onboarding.

Tools used: predictive_fraud, predictive_behaviour
Model: Claude Haiku 4.5

Example invocation:

@chainaware-rwa-investor-screener Screen this investor wallet for RWA platform onboarding: 0xINVESTOR...ABC

Output includes:
- Fraud / AML risk classification
- Investment behavioural profile (institutional patterns, retail speculation, arbitrage)
- Suitability assessment for regulated RWA products
- Onboarding recommendation (Approve / Enhanced due diligence / Decline)
- Compliance notes for audit trail