A sophisticated multi-agent orchestration that automates the discovery, strategy design, and execution of liquidity provider positions in decentralized finance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install full-lp-workflow
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 full-lp-workflow 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 Full LP Workflow?
The Full LP Workflow is the most advanced multi-agent pipeline within the Openclaw Skills ecosystem. It is designed to transform a simple user intent, such as deploying capital for optimal yield, into a fully researched and risk-assessed technical execution. By chaining six specialized sub-agents—including scanners, strategists, and risk assessors—it provides a professional-grade automation layer for DeFi liquidity management.
This skill eliminates the friction of manual pool research and range setting by providing compound context across the entire lifecycle. Each agent builds on the previous one's findings, ensuring that the risk assessment is based on the actual strategy designed, not generic data. With two integrated user confirmation points, it balances autonomous efficiency with strict human-in-the-loop capital control.
Full LP Workflow Use Cases
- Deploying specific capital amounts into the highest risk-adjusted yield opportunities across multiple chains.
- Executing end-to-end liquidity provision including automated token swapping for correct pair ratios.
- Performing deep-dive risk assessments on Uniswap V3 positions before committing assets.
- Automating the transition from idle wallet balances to active, monitored LP positions with rebalancing strategies.
How Full LP Workflow Works
- The opportunity-scanner identifies and ranks the top 5 yield opportunities across supported chains based on user risk tolerance.
- The lp-strategist designs an optimal tick range and rebalancing plan for the user's selected pool.
- The risk-assessor performs a safety check, evaluating impermanent loss potential and smart contract integrity.
- If the user does not hold the necessary tokens, the trade-executor calculates and proposes a swap to prepare the assets.
- The liquidity-manager handles approvals and executes the position entry after receiving explicit user confirmation.
- The portfolio-analyst verifies the transaction and generates a comprehensive impact report with ongoing monitoring instructions.
Full LP Workflow Setup
To utilize this skill, ensure your Openclaw Skills environment is connected to a supported wallet and the Uniswap MCP is active. Configure your safety limits to match your intended deployment size.
# Check safety status before starting
mcp__uniswap__check_safety_status --limit 10000
# Trigger the workflow via natural language
# "Deploy $5000 into the best LP opportunity on Ethereum"
Full LP Workflow Data Schema & Taxonomy
The skill organizes its multi-agent output into a structured data lifecycle:
| Component | Data Managed | Format |
|---|---|---|
| Discovery | Pool address, fee APY, TVL, and volume trends | Array of Objects |
| Strategy | Lower/upper ticks, fee tiers, and estimated IL | JSON Object |
| Risk Report | Safety status (APPROVE/VETO) and risk ratings | Technical Report |
| Position | Token amounts, position ID, and transaction hash | Metadata Object |
Full LP Workflow Advanced Features
- Multi-Agent Pipeline: Orchestrates six distinct AI agents with inherited context for high-precision decision making.
- Conditional Swap Logic: Automatically detects balance deficiencies and prepares the exact token ratio required for the LP range.
- Compound Risk Assessment: Evaluates the specific strategy parameters against real-time market volatility.
- State Preservation Recovery: If a transaction fails mid-pipeline, the skill retains all research data to allow for manual or automated recovery.
SKILL.md
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