ChainaAware Behavioral Prediction

Use this skill whenever a user asks about wallet safety, fraud risk, rug pull detection, wallet behavior analysis, DeFi personalization, on-chain reputation scoring, AML checks, token ranking by holder quality, airdrop screening, lending risk, token launch auditing, or AI agent trust scoring. Triggers on questions like: is this wallet safe?, will this pool rug pull?, what will this address do next?, score this wallet, detect fraud for address, personalize my DeFi agent, rank this token, top AI tokens, best holders of this token, check this contract, is this token safe?, profile this wallet, KYC this address, pre-screen this user, AML check this wallet, is this address suspicious?, screen this wallet before onboarding, what is the risk score of this address?, analyze on-chain behavior, is this LP safe to deposit?, will this contract rug?, what DeFi products suit this wallet?, segment this user, what is this wallet's experience level?, find strong token holders, which token has the best community?,rank tokens by holder quality, should we list this token?, audit this launch, is this deployer trustworthy?, vet this IDO, launch safety check, screen this airdrop list, filter bots from airdrop, rank these wallets for token distribution, fair airdrop allocation, assess this borrower, what collateral ratio for this wallet?, lending risk for 0x..., what interest rate for this borrower?, should I lend to this wallet?, screen this AI agent, is this agent wallet safe?, agent trust score for 0x..., check the feeder wallet for this agent, can I trust this agent?, route this wallet to onboarding, is this user a beginner?, skip onboarding for this wallet?, or any request to analyze a blockchain wallet address, smart contract, token, or AI agent for risk, behavior, intent, community strength, or trustworthiness. Also use when integrating the ChainAware MCP server into Claude Code, Cursor, ChatGPT, or any MCP-compatible AI agent framework.

ChainAware.ai@chainaware

Install

openclaw skills install @chainaware/chainaware-behavioral-prediction

ChainAware Behavioral Prediction MCP

What This Skill Does

The ChainAware Behavioral Prediction MCP connects any AI agent to a continuously updated Web3 behavioral intelligence layer: 14M+ wallet profiles across 8 blockchains, built from 1.3 billion+ predictive data points. It delivers ten capabilities via a single MCP endpoint:

Fraud Detection — predict fraudulent wallet behavior before it happens (~98% accuracy on ETH)
Batch Fraud Detection — async batch job for fraud screening large wallet lists; returns job_id + signature
Behavioral Analysis — profile wallet intent, risk tolerance, experience, and next likely actions
Batch Behavioral Analysis — async batch job for behavioral profiling large wallet lists; returns job_id + signature
Batch Job Status — poll the progress of any running batch job
Batch Job Results — retrieve full per-wallet results from a completed or partial batch job
Rug Pull Detection — forecast whether a smart contract or liquidity pool will rug pull
Credit Score — crypto credit/trust score (1–9) combining fraud probability and social graph analysis
Token Rank List — rank tokens by holder community strength across chains and categories
Token Rank Single — deep-dive into a single token's community quality and top holders

Unlike forensic blockchain tools that describe the past, this MCP is predictive — it tells your agent what is about to happen.

MCP Server URL: https://prediction.mcp.chainaware.ai/sse
GitHub: https://github.com/ChainAware/behavioral-prediction-mcp
Website: https://chainaware.ai
Pricing / API Key: https://chainaware.ai/pricing
Twitter: https://x.com/ChainAware/
LinkedIn: https://www.linkedin.com/company/chainaware
Blog: https://chainaware.ai/blog
Learn: https://chainaware.ai/learn
Examples: https://github.com/ChainAware/examples
Fraud Detection Accuracy: 98% backtesting verified
Rug Pull Detection Accuracy: 90.1% backtesting verified
Recognition: CB Insights Fraud Prevention Market Map (2026) · BNB Chain AI Landscape (2025) · BNB Chain Kickstart (2025) · Google Cloud $250k Grant (2025) · AWS Fintech Accelerator (2024) · Safary Club Web3 Growth Landscape (2024)

Capabilities

Fraud Detection — predict fraudulent wallet behavior before it happens (~98% accuracy on ETH)
Batch Fraud Detection — async batch fraud screening for large wallet lists; fire-and-fetch pattern via predictive_fraud_batch → check_job_status → get_job_results
Behavioral Analysis — profile wallet intent, risk tolerance, experience, and next likely actions across DeFi, NFT, and trading segments
Batch Behavioral Analysis — async batch behavioral profiling for large wallet lists; same fire-and-fetch pattern via predictive_behaviour_batch → check_job_status → get_job_results
Rug Pull Detection — forecast whether a smart contract or liquidity pool will rug pull
Credit Score — crypto credit/trust score (1–9) combining fraud probability and social graph analysis for DeFi lending decisions
Token Rank List — rank tokens by holder community strength across ETH, BNB, BASE, and Solana
Token Rank Single — deep-dive into a specific token's community quality and top holders

When to Use This Skill

User asks about wallet safety, fraud risk, or suspicious activity
User wants to screen a wallet, contract, or LP before interacting with it
User needs AML/compliance checks on a blockchain address
User wants behavioral profiling or DeFi personalization for a wallet
User asks about token quality, community strength, or holder analysis
User is building a DeFi platform, AI agent, launchpad, or compliance tool
User wants to integrate the ChainAware MCP into their codebase

When NOT to Use This Skill

User asks about general blockchain data (balances, transaction history) → use a block explorer
User wants real-time price data or market cap → use a market data API
User wants to analyze smart contract code for bugs → use a code auditing tool
For complex behavioural analysis (deep wallet profiling including fraud signals) → escalate to chainaware-wallet-auditor subagent
For batch screening of many wallets → use batch MCP tools (predictive_fraud_batch / predictive_behaviour_batch) for large lists, or chainaware-fraud-detector subagent for small lists
For marketing personalization → use chainaware-wallet-marketer subagent

Supported Blockchains

Tool	Networks
`predictive_fraud`	ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
`predictive_fraud_batch`	ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
`predictive_behaviour`	ETH, BNB, BASE, HAQQ, SOLANA
`predictive_behaviour_batch`	ETH, BNB, BASE, HAQQ, SOLANA
`check_job_status`	Network-agnostic (uses job_id + signature)
`get_job_results`	Network-agnostic (uses job_id + signature)
`predictive_rug_pull`	ETH, BNB, BASE, HAQQ
`credit_score`	ETH
`token_rank_list`	ETH, BNB, BASE, SOLANA
`token_rank_single`	ETH, BNB, BASE, SOLANA

Step-by-Step Workflow

For wallet fraud screening

Confirm inputs — wallet address and network. If network is missing, ask.
Call predictive_fraud with the wallet address and network.
Interpret probabilityFraud using the threshold table below.
Scan forensic_details for negative flags (mixer use, sanctioned entities, darknet, etc.).
Report status, score, and any forensic flags in plain language.

For behavioral profiling / personalization

Confirm inputs — wallet address and network.
Call predictive_behaviour with the wallet address and network.
Extract key signals: intention.Value (Prob_Trade/Stake/Bridge/NFT_Buy), experience.Value, categories, recommendation.
Classify the wallet by dominant category and intent signal.
Generate personalized recommendations or next-best-action based on the profile.

For rug pull / contract safety checks

Confirm inputs — smart contract or LP address and network.
Optionally call predictive_fraud on the deployer address first for extra signal.
Call predictive_rug_pull with the contract address.
Interpret probabilityFraud and scan forensic_details for liquidity and contract risk flags.
Apply the Deployer Risk Amplifier: if deployer fraud score ≥ 0.5, escalate overall risk one level.
Report verdict with supporting forensic evidence.

For token ranking / discovery

Identify the request — list of tokens or single token deep-dive?
For lists: call token_rank_list with appropriate category, network, sort_by: communityRank, sort_order: DESC.
For single tokens: call token_rank_single with contract_address and network.
Report communityRank, normalizedRank, totalHolders, and top holder profiles.

For batch fraud or behavioural screening (large wallet lists)

Schedule — call predictive_fraud_batch or predictive_behaviour_batch with the wallet list and network.
Store both job_id and signature from the response — required for all follow-up calls.
Poll — call check_job_status with job_id + signature until status is completed or partial.
- Status pending or processing → wait and retry.
- Status partial → some wallets failed but results are available for completed items.
Retrieve — call get_job_results with job_id + signature to fetch the full per-wallet data.
Process — the data array returns the same schema as single-wallet predictive_behaviour / predictive_fraud results.

Never call get_job_results while status is still pending or processing. The job expires_at timestamp in the status response indicates how long results are retained.

For full due diligence (multi-tool)

Call predictive_fraud → get fraud score and forensic flags
Call predictive_behaviour → get behavioral profile and intent
Call predictive_rug_pull (if a contract address) → get contract risk
Synthesize all three into a unified verdict with risk level and recommendation

For complex due diligence workflows, escalate to the chainaware-wallet-auditor subagent.

Risk Score Thresholds

Score Range	Label	Recommended Action
0.00 – 0.20	🟢 Low Risk	Safe to proceed
0.21 – 0.50	🟡 Medium Risk	Proceed with caution, monitor
0.51 – 0.80	🔴 High Risk	Block or require additional verification
0.81 – 1.00	⛔ Critical Risk	Reject immediately

Available Tools

1. `predictive_fraud` — Fraud Detection

Forecasts the probability that a wallet will engage in fraudulent activity. Includes AML checks. Use when a user wants to screen a wallet before interacting with it.

Inputs:

apiKey (string, required) — ChainAware API key
network (string, required) — e.g. ETH, BNB, BASE
walletAddress (string, required) — the wallet to evaluate

Key output fields:

status — "Fraud", "Not Fraud", or "New Address"
probabilityFraud — decimal 0.00–1.00
forensic_details — deep on-chain breakdown

Example prompts that trigger this tool:

"Is it safe to interact with 0xABC... on Ethereum?"
"What is the fraud risk of this BNB wallet?"
"Run an AML check on this address."
"Screen this wallet before onboarding."
"Is this address on any sanctions list?"
"Pre-screen this user's wallet for compliance."

2. `predictive_behaviour` — Behavioral Analysis & Personalization

Profiles a wallet's on-chain history and predicts its next actions.

Inputs:

apiKey (string, required)
network (string, required)
walletAddress (string, required)

Key output fields:

intention — predicted next actions (Prob_Trade, Prob_Stake, Prob_Bridge, Prob_NFT_Buy — High/Medium/Low)
recommendation — personalized action suggestions
categories — behavioral segments (DeFi Lender, NFT Trader, Bridge User, etc.)
riskProfile — risk tolerance and balance age breakdown
experience — experience score 0–10 (beginner → expert)
protocols — which protocols this wallet uses (Aave, Uniswap, GMX, etc.)

Example prompts that trigger this tool:

"What will this wallet do next?"
"Is this user a DeFi lender or an NFT trader?"
"Recommend the best yield strategy for this address."
"What's the experience level of this wallet?"
"Personalize my DeFi agent's response for this user."
"Segment this wallet for my marketing campaign."

3. `predictive_rug_pull` — Rug Pull Detection

Forecasts whether a smart contract or liquidity pool is likely to execute a rug pull.

Inputs:

apiKey (string, required)
network (string, required)
walletAddress (string, required) — smart contract or LP address

Key output fields:

status — "Fraud" or "Not Fraud"
probabilityFraud — decimal 0.00–1.00
forensic_details — on-chain metrics behind the score

Example prompts that trigger this tool:

"Will this new DeFi pool rug pull if I stake my assets?"
"Is this smart contract safe?"
"Check if this launchpad project is legitimate."
"Monitor this LP position for rug pull risk."
"Is this contract deployer trustworthy?"

4. `credit_score` — Crypto Credit Score

Calculates a credit/trust score (1–9) for a wallet by combining fraud probability with social graph analysis. Designed for DeFi lending and any use case needing a fast single-number creditworthiness signal.

Inputs:

apiKey (string, required)
network (string, required) — ETH
walletAddress (string, required) — the wallet to score

Key output fields:

creditData.riskRating — integer 1–9 (1 = highest risk, 9 = highest trust)
creditData.walletAddress — echoed wallet address

riskRating	Label	Lending Interpretation
9	✅ Prime	Highest creditworthiness — best terms
7–8	🟢 Reliable	Low credit risk — standard terms
5–6	🟡 Moderate	Elevated caution — higher collateral
3–4	🔴 High Risk	Restricted terms or decline
1–2	⛔ Very High Risk	Do not lend

Example prompts that trigger this tool:

"What is the credit score for 0xABC...?"
"Is this wallet a reliable borrower?"
"Calculate credit score for this address on ETH."
"Rate this wallet's creditworthiness."
"Trust score for lending — 0xDEF... on BNB."

5. `token_rank_list` — Token Ranking by Holder Strength

Ranks tokens by the quality and strength of their holder community.

Inputs:

limit (string, required) — items per page
offset (string, required) — page number
network (string, required) — ETH, BNB, BASE, SOLANA
sort_by (string, required) — e.g. communityRank
sort_order (string, required) — ASC or DESC
category (string, required) — AI Token, RWA Token, DeFi Token, DeFAI Token, DePIN Token
contract_name (string, required) — token name search (empty string for no filter)

Key output fields:

data.total — total matching tokens
data.contracts[] — array with contractAddress, contractName, ticker, chain, category, communityRank, normalizedRank, totalHolders

Example prompts that trigger this tool:

"What are the top AI tokens on Ethereum?"
"Rank DeFi tokens on BNB by community strength."
"Which RWA tokens have the strongest holder base on BASE?"
"Show me the top 10 tokens by community rank on ETH."
"Compare DePIN tokens across Solana and Ethereum."

6. `token_rank_single` — Single Token Rank & Top Holders

Returns the rank and top holders for a specific token by contract address.

Inputs:

contract_address (string, required) — token contract or mint address
network (string, required) — ETH, BNB, BASE, SOLANA

Key output fields:

data.contract — token details including communityRank, normalizedRank, totalHolders
data.topHolders[] — holder wallet addresses with balance, walletAgeInDays, transactionsNumber, totalPoints, globalRank

Example prompts that trigger this tool:

"What is the token rank for USDT on Ethereum?"
"Who are the top holders of 0xdAC17F... on ETH?"
"How strong is the holder base of this contract on BNB?"
"Show me the best holders of this Solana token."

7. `predictive_fraud_batch` — Batch Fraud Detection (Schedule)

Schedules an async fraud detection job for a list of wallet addresses. Returns a job handle immediately — results are fetched later via check_job_status + get_job_results.

Inputs:

apiKey (string, required) — ChainAware API key
network (string, required) — ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
addresses (array[objects], required) — list of wallet address objects to evaluate

Key output fields:

job_id — unique job identifier (store this)
signature — access token for follow-up calls (store this)
status — always "pending" on schedule response
total_items — number of wallets submitted

Example prompts that trigger this tool:

"Run fraud screening on this list of 500 wallets on ETH."
"Batch AML check for these addresses on BNB."
"Screen all wallets from this CSV for fraud on BASE."

8. `predictive_behaviour_batch` — Batch Behavioral Analysis (Schedule)

Schedules an async behavioral analysis job for a list of wallet addresses. Same fire-and-fetch pattern as predictive_fraud_batch.

Inputs:

apiKey (string, required) — ChainAware API key
network (string, required) — ETH, BNB, BASE, HAQQ, SOLANA
addresses (array[objects], required) — list of wallet address objects to evaluate

Key output fields:

job_id — unique job identifier (store this)
signature — access token for follow-up calls (store this)
status — always "pending" on schedule response
total_items — number of wallets submitted

Example prompts that trigger this tool:

"Profile all wallets in this list on ETH — intent, experience, risk."
"Batch behavioural analysis for these 200 addresses on BASE."
"Run segment analysis across all wallets in this Solana list."

9. `check_job_status` — Batch Job Progress

Checks the progress of a scheduled batch job. Returns counts only — no wallet data. Call this after scheduling a batch job and before fetching results.

Inputs:

job_id (string, required) — from predictive_fraud_batch or predictive_behaviour_batch
signature (string, required) — from the same schedule call

Key output fields:

status — "pending" | "processing" | "partial" | "completed"
completed_items, failed_items, pending_items — progress counts
expires_at — when results will be purged

Status	Meaning	Next Action
`pending`	Queued, not started	Wait and retry
`processing`	Actively running	Wait and retry
`partial`	Some done, some failed	Safe to call `get_job_results`
`completed`	All wallets processed	Call `get_job_results`

10. `get_job_results` — Batch Job Results

Retrieves the full per-wallet results from a completed or partial batch job. Returns the same rich schema as the single-wallet tools. Only call when check_job_status shows completed or partial.

Inputs:

job_id (string, required) — from the schedule call
signature (string, required) — from the same schedule call

Key output fields:

data[] — array of per-wallet results; each entry mirrors predictive_behaviour / predictive_fraud output schema

Example prompts that trigger this tool:

"Get the results for job 0fc5897a on ETH."
"Fetch the completed batch fraud results."
"Retrieve wallet profiles from the batch I scheduled earlier."

Validation Checkpoints

Input Validation

✅ Wallet address provided and non-empty
✅ Network specified and supported for the tool being called (check table above)
✅ CHAINAWARE_API_KEY environment variable is set (not required for check_job_status / get_job_results)
✅ For token_rank_list: limit, offset, sort_by, sort_order, and category all provided
✅ For token_rank_single: both contract_address and network provided
✅ For batch tools: both job_id and signature stored from the schedule response before calling follow-up tools
⚠️ If network is missing, ask the user before proceeding
⚠️ If network is not supported for the requested tool, inform the user and suggest an alternative
⚠️ Never call get_job_results while job status is pending or processing

Output Validation

✅ probabilityFraud is present and in range 0.00–1.00
✅ Risk threshold label applied correctly (see table above)
✅ Forensic flags surfaced in plain language, not raw JSON
✅ Every recommendation cites the specific signal that drove it
✅ Network limitations clearly stated when a tool doesn't support the requested chain
✅ For behavioral profiles: at least intention, experience, and categories included in response
✅ For batch jobs: always report job_id to the user after scheduling; they may need it to check status manually

Example Output

Fraud Check — 0xABC... on ETH

text

🔮 FRAUD ASSESSMENT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Wallet:  0xABC...
Network: ETH
Status:  🟡 MEDIUM RISK

Fraud Probability: 0.34
Risk Level: Medium — proceed with caution

Forensic Highlights:
  • 3 transactions flagged as suspicious
  • No mixer/tumbler activity detected
  • No sanctioned entity connections
  • Wallet age: 187 days

Recommendation: Monitor this wallet. Not safe for large-value
interactions without additional verification.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Behavioral Profile — 0xDEF... on BASE

text

🧠 BEHAVIORAL PROFILE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Wallet:  0xDEF...
Network: BASE

Experience:   7.2/10 — Experienced
Segment:      DeFi Lender, Bridge User
Risk Profile: Balanced

Intent Signals:
  Trade:    High
  Stake:    Medium
  Bridge:   High
  NFT Buy:  Low

Protocols Used: Aave, Uniswap, Across Bridge

Recommendation:
  → Promote yield optimization vaults
  → Highlight cross-chain bridging incentives
  → Skip NFT-focused messaging
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Requirements

API Key — a CHAINAWARE_API_KEY environment variable is required. Obtain one at https://chainaware.ai/pricing
MCP-compatible host — Claude Code, Cursor, Claude Desktop, ChatGPT Connectors, or any MCP client that supports SSE transport
Network awareness — different tools support different blockchains; see the Supported Blockchains table above
No local installation — the MCP server runs remotely at https://prediction.mcp.chainaware.ai/sse; no packages to install

Integration Setup

Claude Code (CLI)

bash

claude mcp add --transport sse chainaware-behavioural-prediction-mcp-server \
  https://prediction.mcp.chainaware.ai/sse \
  --header "X-API-Key: your-key-here"

📚 Docs: https://code.claude.com/docs/en/mcp

Claude Web / Claude Desktop

Go to Settings → Integrations → Add integration
Name: ChainAware Behavioural Prediction MCP Server
URL: https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

📚 Docs: https://platform.claude.com/docs/en/agents-and-tools/remote-mcp-servers

Cursor (`mcp.json`)

json

{
  "mcpServers": {
    "chainaware-behavioural-prediction-mcp-server": {
      "url": "https://prediction.mcp.chainaware.ai/sse",
      "transport": "sse",
      "headers": {
        "X-API-Key": "your-key-here"
      }
    }
  }
}

📚 Docs: https://cursor.com/docs/context/mcp

ChatGPT Connectors

Open ChatGPT Settings → Apps / Connectors → Add Connector
Name: ChainAware Behavioural Prediction MCP Server
URL: https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

Node.js

javascript

import { MCPClient } from "mcp-client";
const client = new MCPClient("https://prediction.mcp.chainaware.ai/");

const fraud = await client.call("predictive_fraud", {
  apiKey: process.env.CHAINAWARE_API_KEY,
  network: "ETH",
  walletAddress: "0xYourWalletAddress"
});

const topTokens = await client.call("token_rank_list", {
  limit: "10", offset: "0", network: "ETH",
  sort_by: "communityRank", sort_order: "DESC",
  category: "AI Token", contract_name: ""
});

Python

python

from mcp_client import MCPClient
import os

client = MCPClient("https://prediction.mcp.chainaware.ai/")
result = client.call("predictive_fraud", {
    "apiKey": os.environ["CHAINAWARE_API_KEY"],
    "network": "ETH",
    "walletAddress": "0xYourWalletAddress"
})

Real-World Use Cases

DeFi Platforms

Risk-adjusted lending — use fraud scores and behavioral profiles to set collateral requirements and interest rates per borrower
Liquidity management — use intent signals to pre-position reserves and prevent pool drain
Yield routing — identify wallets with high yield-seeking intent and route them to optimal vaults

AI Agent Personalization

Give your agent a real-time behavioral profile of each wallet it talks to
Segment users automatically into DeFi Lender, NFT Trader, Bridge User, New Wallet, etc.

Fraud & Compliance

Screen wallets at the point of entry to your Dapp — before any transaction takes place
Run AML monitoring across all active wallets
Detect rug pull contracts at launchpad listing stage

NFT & GameFi

Personalize in-game economies based on a player wallet's on-chain history
Filter bot wallets and wash traders from NFT drops using fraud scores

Tips for Success

Always specify the network — many tools behave differently across chains
Run fraud check first — before any behavioral profiling, gate on fraud score
Combine tools for full due diligence — fraud + behaviour + rug pull together give a complete picture
Use the Deployer Risk Amplifier — a clean contract from a fraudulent deployer is still high risk
For small lists (< 5 wallets) — use subagents like chainaware-fraud-detector or chainaware-airdrop-screener which call single-wallet tools in a loop
For 5+ wallets — use batch tools directly: predictive_fraud_batch or predictive_behaviour_batch → check_job_status → get_job_results
Always store job_id + signature — both are required for every follow-up batch call; losing either means you cannot retrieve results
Surface forensic flags in plain language — never return raw JSON to end users

Related Subagents (Claude Code)

These subagents in .claude/agents/ provide specialized autonomous execution:

Subagent	Use When
`chainaware-wallet-auditor`	Full due diligence — deep behavioural profiling including fraud signals
`chainaware-fraud-detector`	Fast fraud screening, batch wallet checks
`chainaware-rug-pull-detector`	Contract/LP safety with deployer analysis
`chainaware-wallet-marketer`	Personalized marketing messages per wallet segment
`chainaware-reputation-scorer`	Reputation score 0–1000
`chainaware-aml-scorer`	AML compliance scoring 0–100
`chainaware-trust-scorer`	Simple composable trust score 0.00–1.00
`chainaware-credit-scorer`	Crypto credit score 1–9 for lending and creditworthiness decisions
`chainaware-wallet-ranker`	Wallet experience rank and leaderboard
`chainaware-whale-detector`	Whale tier classification for VIP treatment
`chainaware-onboarding-router`	Route wallets to beginner / intermediate / skip onboarding
`chainaware-token-ranker`	Discover and rank tokens by holder community strength
`chainaware-token-analyzer`	Single token deep-dive — community rank + top holders
`chainaware-defi-advisor`	Personalized DeFi product recommendations by experience + risk tier
`chainaware-airdrop-screener`	Batch screen wallets for airdrop eligibility, filter bots and fraud
`chainaware-lending-risk-assessor`	Borrower risk grade (A–F), collateral ratio, interest rate tier
`chainaware-token-launch-auditor`	Pre-listing launch safety audit — APPROVED / CONDITIONAL / REJECTED
`chainaware-agent-screener`	AI agent trust score 0–10 via agent + feeder wallet fraud checks
`chainaware-cohort-analyzer`	Segment a batch of wallets into behavioral cohorts with engagement strategies
`chainaware-counterparty-screener`	Real-time pre-transaction go/no-go (Safe / Caution / Block)
`chainaware-governance-screener`	DAO voter Sybil detection and voting weight calculation
`chainaware-sybil-detector`	Bulk Sybil attack detection for DAO votes — ELIGIBLE / REVIEW / EXCLUDE per wallet, pattern flags, and vote multipliers
`chainaware-transaction-monitor`	Real-time transaction risk for autonomous agents — ALLOW / FLAG / HOLD / BLOCK
`chainaware-lead-scorer`	Sales lead qualification — score, tier, conversion probability, outreach angle
`chainaware-upsell-advisor`	Next product recommendation and upsell message for existing users
`chainaware-platform-greeter`	Contextual welcome message per wallet per platform
`chainaware-marketing-director`	Full-cycle campaign orchestrator — segments, leads, whales, per-cohort messages
`chainaware-compliance-screener`	MiCA-aligned compliance report — PASS / EDD / REJECT (~70–75% MiCA coverage)
`chainaware-gamefi-screener`	Web3 game / P2E bot detection, player tier classification, reward eligibility
`chainaware-portfolio-risk-advisor`	Portfolio-level rug pull scan, risk grade (A–F), rebalancing plan
`chainaware-rwa-investor-screener`	RWA investor suitability — QUALIFIED / CONDITIONAL / REFER_TO_KYC / DISQUALIFIED
`chainaware-ltv-estimator`	12-month revenue potential (LTV) as a USD range — tx count × avg tx value × fee rate, scaled by behavioral multipliers. Optional: platform_share, fee_rate

Background Reading

Article	URL
Complete Product Guide	https://chainaware.ai/blog/chainaware-ai-products-complete-guide/
Fraud Detector Guide	https://chainaware.ai/blog/chainaware-fraud-detector-guide/
Rug Pull Detector Guide	https://chainaware.ai/blog/chainaware-rugpull-detector-guide/
Token Rank Guide	https://chainaware.ai/blog/chainaware-token-rank-guide/
Wallet Rank Guide	https://chainaware.ai/blog/chainaware-wallet-rank-guide/
Wallet Auditor Guide	https://chainaware.ai/blog/chainaware-wallet-auditor-how-to-use/
Transaction Monitoring Guide	https://chainaware.ai/blog/chainaware-transaction-monitoring-guide/
Web3 Behavioral Analytics Guide	https://chainaware.ai/blog/chainaware-web3-behavioral-user-analytics-guide/
Credit Score Guide	https://chainaware.ai/blog/chainaware-credit-score-the-complete-guide-to-web3-credit-scoring-in-2026/
Credit Scoring Agent Guide	https://chainaware.ai/blog/chainaware-credit-scoring-agent-guide/
Prediction MCP Developer Guide	https://chainaware.ai/blog/prediction-mcp-for-ai-agents-personalize-decisions-from-wallet-behavior/
Top 5 Ways Prediction MCP Turbocharges DeFi	https://chainaware.ai/blog/top-5-ways-prediction-mcp-will-turbocharge-your-defi-platform/
Why Personalization Is Next for AI Agents	https://chainaware.ai/blog/why-personalization-is-the-next-big-thing-for-ai-agents/
Web3 User Segmentation for DApp Growth	https://chainaware.ai/blog/web3-user-segmentation-behavioral-analytics-for-dapp-growth-2026/
AI-Powered Blockchain Analysis	https://chainaware.ai/blog/ai-powered-blockchain-analysis-machine-learning-for-crypto-security-2026/
Forensic vs AI-Based Crypto Analytics	https://chainaware.ai/blog/forensic-crypto-analytics-versus-ai-based-crypto-analytics/
Web3 Business Potential	https://chainaware.ai/blog/web3-business-potential/

Data & Privacy

What data leaves your environment

Every tool call transmits the following to https://prediction.mcp.chainaware.ai/sse:

Field	Example	Notes
`walletAddress`	`0xABC...`	Pseudonymous on-chain identifier — not PII
`network`	`ETH`	Chain identifier only
`apiKey`	(your key)	Sourced from `CHAINAWARE_API_KEY` env var; never logged

What is NOT sent: names, emails, IP addresses, private keys, raw transaction history, or any off-chain personal data.

API key handling

CHAINAWARE_API_KEY is read from the environment and passed as the apiKey parameter in each tool call. It is never included in output, never written to disk, and never logged by this skill. Treat it as a secret and rotate it regularly.

Integration-specific privacy notes

Claude Code / Cursor: key passed via X-API-Key header — does not appear in URLs or logs
Claude Web / ChatGPT: key must be appended to the SSE URL (?apiKey=...) — these platforms do not support custom SSE headers. Be aware the key will appear in your browser's network tab. Use a restricted-scope key for these integrations.

Operator responsibilities

Wallet addresses are pseudonymous identifiers. Whether they constitute personal data in your jurisdiction depends on your regulatory context (e.g. GDPR, MiCA). Operators processing wallets linked to identified users should perform their own data protection assessment.

Privacy policy: https://chainaware.ai/privacy

Security Notes

Never hard-code API keys in public repositories
The server uses SSE (Server-Sent Events) for streaming responses
Rate limits apply depending on your subscription tier

Error Reference

Code	Meaning
`403 Unauthorized`	Invalid or missing `apiKey`
`400 Bad Request`	Malformed `network` or `walletAddress`
`500 Internal Server Error`	Temporary backend failure — retry after a short delay

Access & Pricing

API key required. Subscribe at: https://chainaware.ai/pricing

ChainaAware Behavioral Prediction

Install

ChainAware Behavioral Prediction MCP

What This Skill Does

Capabilities

When to Use This Skill

When NOT to Use This Skill

Supported Blockchains

Step-by-Step Workflow

For wallet fraud screening

For behavioral profiling / personalization

For rug pull / contract safety checks

For token ranking / discovery

For batch fraud or behavioural screening (large wallet lists)

For full due diligence (multi-tool)

Risk Score Thresholds

Available Tools

1. predictive_fraud — Fraud Detection

2. predictive_behaviour — Behavioral Analysis & Personalization

3. predictive_rug_pull — Rug Pull Detection

4. credit_score — Crypto Credit Score

5. token_rank_list — Token Ranking by Holder Strength

6. token_rank_single — Single Token Rank & Top Holders

7. predictive_fraud_batch — Batch Fraud Detection (Schedule)

8. predictive_behaviour_batch — Batch Behavioral Analysis (Schedule)

9. check_job_status — Batch Job Progress

10. get_job_results — Batch Job Results

Validation Checkpoints

Input Validation

Output Validation

Example Output

Fraud Check — 0xABC... on ETH

Behavioral Profile — 0xDEF... on BASE

Requirements

Integration Setup

Claude Code (CLI)

Claude Web / Claude Desktop

Cursor (mcp.json)

ChatGPT Connectors

Node.js

Python

Real-World Use Cases

DeFi Platforms

AI Agent Personalization

Fraud & Compliance

NFT & GameFi

Tips for Success

Related Subagents (Claude Code)

Background Reading

Data & Privacy

What data leaves your environment

API key handling

Integration-specific privacy notes

Operator responsibilities

Security Notes

Error Reference

Access & Pricing

Related skills

1. `predictive_fraud` — Fraud Detection

2. `predictive_behaviour` — Behavioral Analysis & Personalization

3. `predictive_rug_pull` — Rug Pull Detection

4. `credit_score` — Crypto Credit Score

5. `token_rank_list` — Token Ranking by Holder Strength

6. `token_rank_single` — Single Token Rank & Top Holders

7. `predictive_fraud_batch` — Batch Fraud Detection (Schedule)

8. `predictive_behaviour_batch` — Batch Behavioral Analysis (Schedule)

9. `check_job_status` — Batch Job Progress

10. `get_job_results` — Batch Job Results

Cursor (`mcp.json`)