A high-performance security scanner for detecting rug pulls, honeypots, and holder concentration in crypto tokens.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prism-scanner
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install prism-scanner using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Prism-Scanner: Token Rug Checker?
Prism-Scanner is a specialized security tool designed for the Openclaw Skills ecosystem, providing instant due diligence for any cryptocurrency token. By interfacing with the Strykr PRISM engine, it evaluates critical risk factors such as holder concentration, liquidity locks, and contract authenticity across Solana and EVM chains. This skill is built to empower AI agents and developers, allowing them to automate the vetting process and identify malicious patterns before they result in financial loss.
Integrating this skill into your workflow ensures that every token interaction is backed by deep analytics. Whether you are building a trading bot or a research assistant, Prism-Scanner provides the technical depth required to navigate the volatile DeFi landscape safely. It translates complex blockchain data into a simplified risk score, making it an essential component for anyone utilizing Openclaw Skills for financial automation.
Prism-Scanner: Token Rug Checker Use Cases
- Automate due diligence for new token listings to identify potential honeypots.
- Integrate real-time security scanning into Telegram or Discord community bots.
- Audit holder distribution and top-tier wallet concentration for memecoins.
- Verify liquidity lock status and contract source code validity automatically.
- Enhance agentic trading workflows by adding a security-first validation layer using Openclaw Skills.
How Prism-Scanner: Token Rug Checker Works
- The user provides a token symbol or contract address to the scanner script.
- The skill resolves the input into a canonical form via the PRISM API to ensure data accuracy.
- It concurrently queries multiple endpoints to analyze holder distribution, contract rebrands, and copycat patterns.
- A risk score is calculated based on weighted factors including liquidity depth, token age, and buy/sell tax anomalies.
- The final output is generated as either a human-readable report or a structured JSON object for further processing.
Prism-Scanner: Token Rug Checker Setup
To get started with this skill within the Openclaw Skills directory, follow these steps:
# Navigate to the skill directory
cd prism-scanner
# Configure your environment variables
export PRISM_URL=https://strykr-prism.up.railway.app
export PRISM_API_KEY=your-optional-api-key
# Run a scan for a specific token
./scan.sh [TOKEN_SYMBOL_OR_ADDRESS]
Prism-Scanner: Token Rug Checker Data Schema & Taxonomy
The skill organizes its security findings into a structured report. When using the JSON flag, the data follows this taxonomy:
| Key | Description | Data Type |
|---|---|---|
| risk_score | Overall risk rating (0-100) | Integer |
| contract_verified | Boolean status of source code verification | Boolean |
| holder_stats | Percentage held by top 10 and top 100 wallets | Object |
| liquidity_status | Details on locked LP and total liquidity | Object |
| copycat_detected | Indicator if the token mimics a known scam | Boolean |
Prism-Scanner: Token Rug Checker Advanced Features
- Multi-chain support for seamless scanning across Solana and Ethereum Virtual Machine (EVM) networks.
- Structured JSON output mode for programmatic integration into complex Openclaw Skills automation pipelines.
- Advanced copycat detection using similarity algorithms to identify rebranded scam contracts.
- Real-time holder concentration analysis with deep-dive metrics for top 100 wallets.
- Agent-ready architecture allowing LLMs to trigger scans and interpret risk scores dynamically.
SKILL.md
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