An AI-driven liquidity strategy engine that provides optimal Uniswap V2, V3, or V4 parameters for any token pair.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install optimize-lp
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 optimize-lp 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 LP Optimizer?
The LP Optimizer is a specialized tool designed to maximize returns and manage risk for liquidity providers in the decentralized finance ecosystem. By analyzing historical volatility, on-chain data, and specific pair characteristics, it generates a comprehensive strategy including version selection, fee tiers, and concentration ranges. This skill is a core component of the Openclaw Skills ecosystem, providing actionable insights for DeFi participants seeking to professionalize their liquidity management.
It bridges the gap between complex on-chain data and retail-friendly execution by delegating tasks to a dedicated strategist agent. Whether you are providing liquidity for stablecoins or highly volatile assets, the LP Optimizer ensures your position is structured to balance fee collection against impermanent loss.
LP Optimizer Use Cases
- Determining the best fee tier and range for a specific token pair like ETH/USDC.
- Comparing returns between Uniswap V2, V3, and V4 versions for a given capital amount.
- Designing a rebalance strategy to maintain high time-in-range performance.
- Assessing potential impermanent loss (IL) versus expected fee income before committing capital.
- Tailoring liquidity strategies to specific risk profiles ranging from conservative to aggressive.
How LP Optimizer Works
- Extracts tokens, capital, chain, and risk preference from the user's natural language prompt.
- Triggers the lp-strategist subagent within the Openclaw Skills framework to perform a deep seven-step analysis.
- Classifies the asset pair to determine the volatility profile (e.g., stable-to-stable or volatile-to-volatile).
- Selects the optimal protocol version and fee tier based on current market depth and historical volume.
- Calculates concentrated liquidity ranges aimed at achieving greater than 80% time-in-range.
- Consults a risk-assessor subagent to validate the strategy against the user's specified risk tolerance.
- Generates a detailed report with conservative, moderate, and optimistic APY projections after accounting for impermanent loss.
LP Optimizer Setup
To utilize this skill, ensure your AI agent is configured to access the lp-strategist subagent. No manual installation of external libraries is required if you are using the standard Openclaw Skills environment.
# Example of how to trigger the analysis via a CLI or chat interface
# "What is the best LP strategy for WETH/USDC on Arbitrum with $10,000?"
Ensure that the agent has connectivity to on-chain data providers to fetch real-time pool statistics.
LP Optimizer Data Schema & Taxonomy
The skill organizes its output into a structured recommendation format that includes the following metadata:
| Data Field | Description |
|---|---|
| Recommendation | Protocol version (V2/V3/V4) and specific fee tier (e.g., 0.05% or 0.3%). |
| Pool Metadata | The specific contract address and the blockchain network identified. |
| Range Stats | Price bounds, width percentage, and estimated time-in-range percentage. |
| Financial Projections | Annualized APY and Impermanent Loss (IL) projections across three scenarios. |
| Rebalance Logic | Specific triggers (e.g., price proximity to boundary) and estimated gas costs per transaction. |
| Risk Assessment | A qualitative risk rating (Low, Medium, High) approved by a secondary assessor. |
LP Optimizer Advanced Features
- Multi-agent delegation involving specialized pool-researcher and risk-assessor agents for high-fidelity data.
- Gas-aware strategy generation that factors in rebalance costs to ensure profitability for smaller positions.
- Automated rebalance trigger calculation based on historical price movement and volatility.
- Alternative strategy comparison providing trade-offs between passive V2 management and active V3/V4 concentrated liquidity.
- Seamless integration with execution skills like manage-liquidity within the Openclaw Skills library.
SKILL.md
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