Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Productivity
Uniswap Optimize Lp
Get the optimal LP strategy for a token pair — recommends version (V2/V3/V4), fee tier, range width, and rebalance approach based on pair characteristics, historical data, and risk tolerance. Use when the user asks how to LP, what range to use, or which version/fee tier is best.
skill openclawclawhub Free
Get the optimal LP strategy for a token pair — recommends version (V2/V3/V4), fee tier, range width, and rebalance approach based on pair characteristics, historical data, and risk tolerance. Use when the user asks how to LP, what range to use, or which version/fee tier is best.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- wpank
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Overview
Provides a focused, actionable LP strategy recommendation for a specific token pair. Delegates to the lp-strategist agent, which analyzes pair volatility, evaluates version options, selects the optimal fee tier, calculates the best range width, and designs a rebalance strategy — all backed by on-chain data and risk analysis. This skill answers the question: "How should I LP into X/Y?" with a concrete, implementable answer.
When to Use
Activate when the user asks: "Best LP strategy for ETH/USDC" "How should I LP $10K into ETH/USDC?" "Tight or wide range for WETH/USDC?" "V2 or V3 for this pair?" "What fee tier should I use for UNI/WETH?" "Optimize my LP approach for X/Y" "What range should I set for my ETH/USDC position?" "Should I use concentrated liquidity for this pair?"
Parameters
ParameterRequiredDefaultHow to Extracttoken0Yes—First token: "ETH", "WETH", "USDC", or 0x addresstoken1Yes—Second tokencapitalNo—Amount to LP: "$10K", "5 ETH", "$50,000"chainNoethereum"ethereum", "base", "arbitrum", etc.riskToleranceNomoderate"conservative", "moderate", "aggressive"
Workflow
Extract parameters from the user's request. Identify the token pair, capital amount (if mentioned), chain, and risk tolerance. Delegate to lp-strategist: Invoke Task(subagent_type:lp-strategist) with the parameters. The agent performs a 7-step analysis: Classifies the pair (stable-stable, stable-volatile, volatile-volatile) Evaluates V2 vs V3 vs V4 tradeoffs Selects optimal fee tier with data backing Calculates range width targeting >80% time-in-range Designs rebalance strategy with gas cost analysis Gets independent risk assessment from risk-assessor Produces a recommendation with conservative/moderate/optimistic estimates Present the strategy as a clear, actionable recommendation with specific numbers.
Output Format
LP Strategy for WETH/USDC Recommendation: V3, 0.05% fee tier Pool: 0x88e6A0c2dDD26FEEb64F039a2c41296FcB3f5640 (Ethereum) Range: $1,668 - $2,258 (±15%, medium strategy) Expected time-in-range: ~82% Expected Returns (annualized): Conservative: 8.5% APY (after IL) Moderate: 15.2% APY (after IL) Optimistic: 24.1% APY (after IL) Expected IL: Conservative: 2.1% Moderate: 5.8% Worst case: 12.3% Rebalance Strategy: Trigger: Price within 10% of range boundary Frequency: Every 2-3 weeks (estimated) Gas cost: ~$15 per rebalance Risk Assessment: MEDIUM (approved by risk-assessor) Alternatives Considered: V3 0.3%: Lower APY (8%) but less competition V2: No range management, ~4% APY — good for passive Narrow range (±5%): Higher APY but needs weekly rebalancing Ready to add liquidity? Say "Add liquidity to WETH/USDC"
Important Notes
This skill provides a strategy recommendation, not execution. To act on it, use the manage-liquidity skill. All APY estimates are based on historical data. Past performance does not guarantee future returns. IL estimates are always shown alongside fee APY — never fee APY alone. For small positions (<$1K), the recommendation accounts for gas costs eating into returns. The lp-strategist internally delegates to pool-researcher for data and risk-assessor for risk evaluation.
Error Handling
ErrorUser-Facing MessageSuggested ActionToken not found"Could not find token X on Uniswap."Provide contract addressNo pools exist"No pools found for X/Y on this chain."Try different chain or check tokensInsufficient data"Not enough historical data to produce a reliable strategy."Pool may be too newAgent unavailable"LP strategist is not available."Check agent configuration
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md