Agent skill

Full Lp Workflow

by LeoYeAI in LeoYeAI/openclaw-master-skills

Full LP workflow from opportunity scanning to position entry.

MITAuto-check passedAgent Workflows

Install Full Lp Workflow

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/full-lp-workflow .claude/skills/full-lp-workflow && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
full-lp-workflow
GitHub stars
2.2k
Token cost
~5.8k tokens
SKILL.md length
1,208 words
Files
3
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Full LP workflow from opportunity scanning to position entry.

  • Works in 7 steps: Pre-Flight → Scan Opportunities (opportunity-scanner) → Design Strategy (lp-strategist) → …
  • User has capital and wants end-to-end LP management
  • SKILL.md covers Overview, When to Use, Parameters and Workflow, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Full Lp Workflow is an agent skill from LeoYeAI/openclaw-master-skills. Full LP workflow from opportunity scanning to position entry. Autonomously finds the best LP opportunity, designs a strategy, assesses risk, executes any needed swaps, enters the position, and reports portfolio impact. Use when user has capital and wants end-to-end LP management. Most complex multi-agent orchestration in the system.

Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `README.md` and `_meta.json`).

It sits in Agent Workflows, covering Multi-agent orchestration and End-to-end testing. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • User has capital and wants end-to-end LP management
  • Tasks that involve Multi-agent orchestration
  • Tasks that involve End-to-end testing

Example prompts

  • “/full-lp-workflow”

Requirements

  • Pre-approved tools (allowed-tools): Task(subagent_type:opportunity-scanner), Task(subagent_type:lp-strategist), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), Task(subagent_type:liquidity-manager), Task(subagent_type:portfolio-analyst), mcp__uniswap__check_safety_status, mcp__uniswap__get_agent_balance

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Pre-Flight
  2. Scan Opportunities (opportunity-scanner)
  3. Design Strategy (lp-strategist)
  4. Risk Assessment (risk-assessor)
  5. Swap If Needed (trade-executor) -- Conditional
  6. Enter Position (liquidity-manager)
  7. Confirm & Report (portfolio-analyst)

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Task(subagent_type:opportunity-scanner)
    • Task(subagent_type:lp-strategist)
    • Task(subagent_type:risk-assessor)
    • Task(subagent_type:trade-executor)
    • Task(subagent_type:liquidity-manager)
    • Task(subagent_type:portfolio-analyst)
    • mcp__uniswap__check_safety_status
    • mcp__uniswap__get_agent_balance

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Full Lp Workflow loads about 5.8k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 1,208 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~5.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,208 words, ~5,796 tokens.

Download SKILL.mdSave it as .claude/skills/full-lp-workflow/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
full-lp-workflow
description
Full LP workflow from opportunity scanning to position entry. Autonomously finds the best LP opportunity, designs a strategy, assesses risk, executes any needed swaps, enters the position, and reports portfolio impact. Use when user has capital and wants end-to-end LP management. Most complex multi-agent orchestration in the system.
allowed-tools
Task(subagent_type:opportunity-scanner), Task(subagent_type:lp-strategist), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), Task(subagent_type:liquidity-manager), Task(subagent_type:portfolio-analyst), mcp__uniswap__check_safety_status, mcp__uniswap__get_agent_balance
model
opus

Full LP Workflow

Overview

This is the most complex multi-agent orchestration in the system. It turns a single intent -- "I have $20K, find me the best yield" -- into a fully researched, risk-assessed, optimally-structured LP position through a 6-agent pipeline.

Why this is 10x better than calling agents individually:

  1. End-to-end automation: Manually, you'd need to scan opportunities, research pools, design a strategy, assess risk, potentially swap tokens, add liquidity, and verify -- each requiring different tools and expertise. This does it all in one command.
  2. Intelligent pipeline with compound context: Each agent builds on all prior agents' findings. The lp-strategist doesn't just get a token pair -- it gets the opportunity-scanner's full analysis of why this pool is optimal. The risk-assessor evaluates the actual strategy designed by the lp-strategist, not a generic assessment.
  3. Two user confirmation points: Before spending any money (swap) and before committing capital (LP entry), the skill pauses for explicit user approval. You stay in control.
  4. Conditional swap step: If you don't hold the right tokens for the LP position, the skill automatically handles the token swap -- but only after showing you exactly what it plans to do.
  5. Portfolio impact reporting: After entering the position, the portfolio-analyst shows you exactly how your portfolio changed, with ongoing monitoring instructions.
  6. Failure recovery at every stage: If any agent fails mid-pipeline, you see what was accomplished and get recovery suggestions.

When to Use

Activate when the user says anything like:

  • "I have $20K, find the best LP opportunity and enter"
  • "Autonomous LP: find yield and enter position"
  • "Full LP workflow with $10,000"
  • "Find me the best yield and set up a position"
  • "I want to LP but don't know where -- find the best option"
  • "Put $5K to work in Uniswap -- find the best opportunity"
  • "End-to-end LP: scan, strategize, and enter"
  • "What's the best LP opportunity right now? Set it up for me"

Do NOT use when the user already knows which pool they want (use manage-liquidity instead), just wants strategy comparison without execution (use lp-strategy), or just wants to scan without entering (use scan-opportunities).

Parameters

ParameterRequiredDefaultHow to Extract
capitalYes--Total capital to deploy: "$20,000", "10 ETH", "$5K"
chainNoallTarget chain or "all" for cross-chain scan
riskToleranceNomoderate"conservative", "moderate", "aggressive"
pairPreferenceNo--Optional token pair preference: "ETH/USDC", "stablecoin pairs"
excludeTokensNo--Tokens to exclude: "PEPE, SHIB" (avoid memecoins, etc.)
capitalTokenNoauto-detectWhat token the capital is in: "USDC", "ETH", auto-detect from wallet

If the user doesn't provide capital amount, ask for it -- never guess how much to deploy.

Workflow

                          FULL LP WORKFLOW PIPELINE
  ┌─────────────────────────────────────────────────────────────────────┐
  │                                                                     │
  │  Step 1: SCAN (opportunity-scanner)                                 │
  │  ├── Scan LP opportunities across chains                            │
  │  ├── Filter by risk tolerance and capital size                      │
  │  ├── Rank top 3-5 by risk-adjusted yield                           │
  │  └── Output: Ranked Opportunity List                                │
  │          │                                                          │
  │          ▼ USER CHOICE POINT                                        │
  │  ┌───────────────────────────────────────────────────┐              │
  │  │  Present top opportunities to user.               │              │
  │  │  User picks one OR accepts recommendation (#1).   │              │
  │  └───────────────────────────────────────────────────┘              │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 2: STRATEGIZE (lp-strategist)                                 │
  │  ├── Design optimal strategy for chosen opportunity                 │
  │  ├── Version, fee tier, range width, rebalance plan                 │
  │  ├── Conservative/moderate/optimistic APY estimates                 │
  │  └── Output: LP Strategy Recommendation                             │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 3: RISK CHECK (risk-assessor)                                 │
  │  ├── Receives: opportunity data + strategy details                  │
  │  ├── Evaluates: IL, slippage, liquidity, smart contract             │
  │  ├── Decision: APPROVE / CONDITIONAL / VETO / HARD_VETO            │
  │  └── Output: Risk Assessment Report                                 │
  │          │                                                          │
  │          ▼ CONDITIONAL GATE                                         │
  │  ┌───────────────────────────────────────────────────┐              │
  │  │  APPROVE / COND. APPROVE → Continue               │              │
  │  │  VETO / HARD_VETO → STOP with full report         │              │
  │  └───────────────────────────────────────────────────┘              │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 4: SWAP IF NEEDED (trade-executor) -- CONDITIONAL             │
  │  ├── Check wallet balances vs required tokens                       │
  │  ├── If tokens needed: calculate swap amounts                       │
  │  ├── USER CONFIRMATION #1: "Swap X for Y to prepare for LP?"       │
  │  ├── Execute swap(s) if confirmed                                   │
  │  └── Output: Swap result OR "tokens already held"                   │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 5: ENTER POSITION (liquidity-manager)                         │
  │  ├── USER CONFIRMATION #2: "Add liquidity with these params?"      │
  │  ├── Handle approvals (Permit2)                                     │
  │  ├── Add liquidity at recommended range                             │
  │  ├── Wait for transaction confirmation                              │
  │  └── Output: Position ID, amounts deposited, tick range             │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 6: CONFIRM & REPORT (portfolio-analyst)                       │
  │  ├── Verify position was created successfully                       │
  │  ├── Show portfolio impact (before vs after)                        │
  │  ├── Monitoring instructions                                        │
  │  └── Output: Portfolio Report + Next Steps                          │
  │                                                                     │
  └─────────────────────────────────────────────────────────────────────┘
Step 0: Pre-Flight

Before starting the pipeline:

  1. Check safety status via mcp__uniswap__check_safety_status -- verify spending limits can accommodate the capital amount.
  2. Check wallet balance via mcp__uniswap__get_agent_balance on the target chain(s) -- verify the wallet has the stated capital.
  3. If either check fails, stop and inform the user before wasting agent compute.
Step 1: Scan Opportunities (opportunity-scanner)

Delegate to Task(subagent_type:opportunity-scanner) with:

Scan for LP opportunities with these parameters:
- Available capital: {capital}
- Chains: {chain or "all supported chains"}
- Risk tolerance: {riskTolerance}
- Type: "lp" (LP opportunities only)
- Minimum TVL: $50,000
- Top N: 5

Additional filters:
- Pair preference: {pairPreference or "none"}
- Exclude tokens: {excludeTokens or "none"}

Return the top 5 LP opportunities ranked by risk-adjusted yield.
Each opportunity must include fee APY, estimated IL, risk-adjusted yield,
TVL, volume, and risk rating.

Present to user with a choice:

text
Step 1/6: Opportunity Scan Complete

  Found 5 LP opportunities (filtered from 200+ pools):

  #  Pool                Chain    Fee APY  Est. IL  Net APY  Risk    TVL
  1  WETH/USDC 0.05%     ETH      21%      -6%      15%     MEDIUM  $332M
  2  WETH/USDC 0.05%     Base     18%      -5%      13%     MEDIUM  $45M
  3  ARB/WETH 0.30%      Arb      35%      -12%     23%     HIGH    $12M
  4  USDC/USDT 0.01%     ETH      8%       -0.1%    7.9%    LOW     $200M
  5  cbETH/WETH 0.05%    Base     12%      -3%      9%      LOW     $28M

  Recommended: #1 (WETH/USDC 0.05% on Ethereum) — best balance of yield and risk

  Which opportunity would you like to pursue? (1-5, or "1" to accept recommendation)

Wait for user selection before proceeding.

Step 2: Design Strategy (lp-strategist)

Delegate to Task(subagent_type:lp-strategist) with the chosen opportunity:

Design an optimal LP strategy for this opportunity:

Opportunity details (from opportunity-scanner):
{Full opportunity data from Step 1 for chosen pool}

LP parameters:
- Capital: {capital}
- Risk tolerance: {riskTolerance}
- Chain: {chain of chosen opportunity}

Provide:
1. Recommended version and fee tier (with rationale)
2. Optimal tick range (lower/upper prices, width %)
3. Conservative/moderate/optimistic APY and IL estimates
4. Rebalance strategy (trigger, frequency, estimated cost)
5. Comparison to the next-best alternative

Present to user:

text
Step 2/6: Strategy Designed

  Pool:     WETH/USDC 0.05% (V3, Ethereum)
  Range:    $1,700 - $2,300 (±15%, medium width)
  Expected: 15% net APY (moderate estimate)

  Strategy Details:
    Fee APY (moderate): 21%
    Estimated IL: -6%
    Net APY: 15%
    Rebalance: every 2-3 weeks (trigger: price within 10% of boundary)
    Rebalance cost: ~$15/rebalance on Ethereum

  Proceeding to risk assessment...
Step 3: Risk Assessment (risk-assessor)

Delegate to Task(subagent_type:risk-assessor) with compound context:

Evaluate risk for this LP strategy:

Operation: add liquidity
Token pair: {token0}/{token1}
Pool: {pool address}
Chain: {chain}
Capital: {capital}
Risk tolerance: {riskTolerance}

Strategy context (from lp-strategist):
{Full strategy recommendation from Step 2}

Opportunity context (from opportunity-scanner):
{Key metrics from Step 1: TVL, volume trend, risk rating}

Evaluate: impermanent loss risk, slippage risk (entry/exit), liquidity risk,
smart contract risk. Provide APPROVE/CONDITIONAL_APPROVE/VETO/HARD_VETO.

Conditional gate (same logic as research-and-trade):

DecisionAction
APPROVEPresent risk summary, proceed to Step 4
CONDITIONAL_APPROVEShow conditions, ask user if they accept
VETOSTOP. Show opportunity + strategy + veto reason. Suggest fallbacks.
HARD_VETOSTOP. Show reason. Non-negotiable.

Present to user (APPROVE case):

text
Step 3/6: Risk Assessment Passed

  Decision: APPROVE
  Composite Risk: MEDIUM
  IL Risk: MEDIUM (8.2% annual estimate for volatile pair)
  Slippage: LOW (deep pool, entry < 0.1% impact)
  Liquidity: LOW (TVL 6,640x your position)
  Smart Contract: LOW (V3, established pool)

  Proceeding to prepare tokens...
Step 4: Swap If Needed (trade-executor) -- Conditional

Check wallet balances against required token amounts for the LP position:

  1. The strategy specifies how much of each token is needed (e.g., 50/50 split for a centered range).
  2. Check current wallet holdings via mcp__uniswap__get_agent_balance.
  3. Calculate what swaps are needed (if any).

If no swaps needed:

text
Step 4/6: Token Preparation — Skipped

  You already hold sufficient WETH and USDC for this position.
  No swaps required.

If swaps needed, present and ask USER CONFIRMATION #1:

text
Step 4/6: Token Preparation Required

  Your position needs:
    0.5 WETH  (~$980)
    980 USDC  (~$980)

  You currently hold:
    2.0 WETH  ($3,920)
    0 USDC    ($0)

  Proposed swap:
    Sell 0.5 WETH → Buy ~980 USDC
    Estimated slippage: 0.05%
    Gas: ~$8

  Approve this swap to prepare tokens for LP? (yes/no)

Only execute the swap if the user explicitly confirms. If the user declines, stop the pipeline and present what was accomplished (scan + strategy + risk assessment).

Step 5: Enter Position (liquidity-manager)

Present the full LP entry details and ask USER CONFIRMATION #2:

text
Step 5/6: Ready to Add Liquidity

  Pool:     WETH/USDC 0.05% (V3, Ethereum)
  Deposit:
    0.5 WETH  (~$980)
    980 USDC  (~$980)
    Total:    ~$1,960

  Range:
    Lower: $1,700 (tick -204714)
    Upper: $2,300 (tick -199514)
    Current: $1,963 — IN RANGE
    Width: ±15% (medium)

  Expected Fee APY: ~15-21% (based on 7d pool data)

  Add liquidity with these parameters? (yes/no)

Only proceed if the user confirms. Then delegate to Task(subagent_type:liquidity-manager):

Add liquidity to this pool:

Pool: {pool address}
Chain: {chain}
Version: {version}
Token0: {token0 address} — Amount: {amount0}
Token1: {token1 address} — Amount: {amount1}
Tick lower: {tick_lower}
Tick upper: {tick_upper}

Strategy context:
- Range strategy: {medium/narrow/wide}
- From lp-strategist recommendation

Execute: handle approvals, simulate, route through safety-guardian, add liquidity,
wait for confirmation. Return position ID and actual amounts deposited.
Step 6: Confirm & Report (portfolio-analyst)

Delegate to Task(subagent_type:portfolio-analyst) with the new position:

Report on the portfolio impact of this new LP position:

New position:
- Position ID: {position_id from Step 5}
- Pool: {pool_address}
- Chain: {chain}
- Tokens deposited: {amounts}
- Tick range: {lower} to {upper}
- Transaction: {tx_hash}

Wallet address: {wallet_address}

Provide:
1. New position details and current status (in-range confirmation)
2. Portfolio impact (if other positions exist, show before/after composition)
3. Total portfolio value across all chains
4. Monitoring recommendations

Present final result:

text
Step 6/6: Position Confirmed & Portfolio Updated

  New Position: #456789
  Pool:     WETH/USDC 0.05% (V3, Ethereum)
  Status:   IN RANGE
  Value:    $1,960

  Deposited:
    0.5 WETH  ($980)
    980 USDC  ($980)

  Range:
    $1,700 — $2,300 (current: $1,963)

  Expected Returns:
    Fee APY: 15-21% (moderate-optimistic)
    Est. IL: -5 to -8% annualized
    Net: 9-15% annualized

  Portfolio Impact:
    Total LP Value:  $1,960 (new) + $45,000 (existing) = $46,960
    LP Allocation:   42% WETH, 35% USDC, 23% other
    Chain Split:     85% Ethereum, 15% Base

  ──────────────────────────────────────
  Pipeline Summary
  ──────────────────────────────────────
  Scan:      5 opportunities found, #1 selected
  Strategy:  V3 0.05%, ±15% range, bi-weekly rebalance
  Risk:      APPROVED (MEDIUM composite)
  Swap:      0.5 WETH → 980 USDC (preparation)
  Position:  #456789 — IN RANGE
  Cost:      $15.20 total gas (swap + LP entry)

  Next Steps:
  - Monitor: "How are my positions doing?"
  - Rebalance when needed: "Rebalance position #456789"
  - Collect fees: "Collect fees from position #456789"
Show full SKILL.md (506 more words)Show less

Critical Decision Points

These are the moments where the skill must stop and ask rather than assume:

SituationAction
Capital amount not specifiedAsk: "How much capital would you like to deploy?"
Multiple good opportunitiesPresent top 3-5, let user choose
Risk-assessor VETOStop. Show research + strategy + veto reason.
Swap needed for token preparationUSER CONFIRMATION #1: show swap details, ask to proceed
Ready to add liquidityUSER CONFIRMATION #2: show position details, ask to proceed
Capital exceeds safety spending limitStop. Show limit. Suggest reducing amount or adjusting limits.
Wallet doesn't have stated capitalStop. Show actual balance. Ask to adjust amount.
Best opportunity is on a different chain than wallet fundsExplain cross-chain situation, suggest bridge or chain choice

Partial Completion Recovery

If the pipeline fails mid-way, report what was accomplished and what remains:

text
LP Workflow — Partial Completion

  Completed:
    [done] Step 1: Scan — 5 opportunities found, #1 selected
    [done] Step 2: Strategy — V3 0.05%, ±15% range designed
    [done] Step 3: Risk — APPROVED (MEDIUM)
    [FAIL] Step 4: Swap — Transaction reverted (insufficient gas)

  Remaining:
    [ ] Step 5: Add liquidity
    [ ] Step 6: Portfolio report

  Recovery Options:
    - Ensure sufficient ETH for gas and retry: "Continue LP workflow"
    - Add liquidity manually with the strategy above: "Add liquidity to WETH/USDC 0.05%"
    - Start over: "Full LP workflow with $X"

Output Format

Successful Pipeline (all 6 steps)
text
Full LP Workflow Complete

  Opportunity: {pool_pair} {fee}% on {chain}
  Strategy:    {version}, {range_width} range, {rebalance_frequency} rebalance
  Risk:        {decision} ({composite_risk})
  Position:    #{position_id} — {status}
  Value:       ${total_value}

  Returns (moderate estimate):
    Fee APY: {fee_apy}%
    Est. IL: {il}%
    Net APY: {net_apy}%

  Cost: ${total_gas} gas across {n} transactions

  Pipeline: Scan -> Strategy -> Risk -> Swap -> Enter -> Confirm (all passed)

Important Notes

  • This is the most complex skill. It orchestrates 6 agents with 2 user confirmation points and a conditional swap step. Each agent receives compound context from all predecessors.
  • Two explicit confirmations: Before any swap and before adding liquidity. The user must say "yes" at both gates.
  • Conditional swap: Step 4 only executes if the user doesn't already hold the right tokens. If they do, it's skipped entirely.
  • Chain considerations: If scanning "all" chains, the best opportunity might be on a chain where the user's funds aren't located. The skill should flag this and suggest bridging or narrowing the chain filter.
  • Gas budget: On Ethereum, the full pipeline (swap + LP) costs $15-50 in gas. For small positions (< $1K), warn that gas costs significantly eat into returns.
  • Never auto-execute: Despite being an "autonomous" workflow, every spend of user capital requires explicit confirmation.

Error Handling

ErrorUser-Facing MessageSuggested Action
No opportunities found"No LP opportunities match your criteria on {chain}."Broaden chain filter or lower minTVL
Opportunity-scanner fails"Opportunity scan failed. Cannot identify LP targets."Try scan-opportunities directly
LP-strategist fails"Strategy design failed for {pool}. Scan results preserved."Try lp-strategy or optimize-lp directly
Risk-assessor VETO"Risk assessment vetoed: {reason}. Strategy: {summary}."Pick a different opportunity or reduce size
Risk-assessor HARD_VETO"Strategy blocked: {reason}. This cannot be overridden."Choose a lower-risk opportunity
Insufficient balance for swap"Not enough {token} to prepare for LP. Have {X}, need {Y}."Reduce capital or acquire tokens
Swap execution fails"Token swap failed: {reason}. Strategy and risk data preserved."Retry or swap manually
Liquidity add fails"Failed to add liquidity: {reason}. Tokens are still in your wallet."Retry or use manage-liquidity directly
Position not confirmed"Position created but not yet confirmed on-chain. Check back in a moment."Wait and check with track-performance
Safety spending limit exceeded"Capital of ${X} exceeds spending limit of ${Y}."Reduce amount or adjust safety config
Wallet not configured"No wallet configured. Cannot execute transactions."Set up wallet with setup-agent-wallet
User declines swap confirmation"Token swap cancelled. Strategy preserved — you can proceed manually."Use manage-liquidity with manual prep
User declines LP confirmation"LP entry cancelled. All prior research is shown above."Adjust parameters and retry

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/full-lp-workflow of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Full Lp Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Full Lp Workflow compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Full Lp Workflow this skillLeoYeAI/openclaw-master-skills2.2k—~5.8kAutomated safety check: PassMIT
Orca CLIstablyai/orca88k2 repos~593Automated safety check: PassMIT
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Paseo Committeegetpaseo/paseo20k1 repos~496Automated safety check: PassCustom licence
Mission Control Agent APIbuilderz-labs/mission-control6.3k—~2.1kAutomated safety check: PassMIT

Similar skills

  • Orca CLI

    stablyai/orca

    Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…

    88k GitHub starsUsed in 2 repos~593 tokens
    Agent WorkflowsAuto-check passed
  • Launches one separate agent through Paseo to give a second opinion on the current task, with a self-contained briefing and no permission to edit files.

    20k GitHub starsUsed in 1 repo~756 tokens
    Agent WorkflowsAuto-check passed
  • O2 Review Loop

    openobserve/openobserve

    Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.

    22k GitHub stars~3.7k tokensUpdated today
    Agent WorkflowsAuto-check passed
  • Paseo Committee

    getpaseo/paseo

    Forms a two-agent committee with contrasting profiles to analyze a stuck problem in parallel, reconcile their views and return a consensus plan without editing files.

    20k GitHub starsUsed in 1 repo~496 tokens
    Agent WorkflowsAuto-check passed
  • Mission Control Agent API

    builderz-labs/mission-control

    Teaches an agent to use the Mission Control dashboard API: register, send heartbeats, fetch assigned tasks, report progress and disconnect, with API key auth.

    6.3k GitHub stars~2.1k tokensUpdated 10 days ago
    Agent WorkflowsAuto-check passed
  • Paseo Agent Handoff

    getpaseo/paseo

    Hands off the current task, including context, decisions and failed attempts, to a fresh agent through Paseo by writing a self-contained briefing prompt and launching that agent.

    20k GitHub starsUsed in 1 repo~606 tokens
    Agent WorkflowsAuto-check passed

More from LeoYeAI/openclaw-master-skills

All 1,235 skills in this repo
  • DevOps Pipeline Management

    LeoYeAI/openclaw-master-skills

    Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.

    2.2k GitHub stars~4.2k tokensUpdated 2 mo ago
    Auto-check: notes
  • Feishu Document Collaboration

    LeoYeAI/openclaw-master-skills

    Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.

    2.2k GitHub stars~2k tokensUpdated 2 mo ago
    Auto-check passed
  • Files Memory System

    LeoYeAI/openclaw-master-skills

    Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.

    2.2k GitHub stars~3.8k tokensUpdated 2 mo ago
    Auto-check passed
  • GEO-Claw AI Visibility Agent

    LeoYeAI/openclaw-master-skills

    Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.

    2.2k GitHub stars~4.7k tokensUpdated 2 mo ago
    Auto-check passed
  • Google Workspace CLI

    LeoYeAI/openclaw-master-skills

    Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.

    2.2k GitHub stars~2.6k tokensUpdated 2 mo ago
    Auto-check: notes
  • HealthFit Health Advisors

    LeoYeAI/openclaw-master-skills

    Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.

    2.2k GitHub stars~4.4k tokensUpdated 2 mo ago
    Auto-check passed

Categories

Questions about Full Lp Workflow

What does Full Lp Workflow do?

Full LP workflow from opportunity scanning to position entry. Full Lp Workflow is an agent skill from LeoYeAI/openclaw-master-skills. Full LP workflow from opportunity scanning to position entry.

When should I use Full Lp Workflow?

Full Lp Workflow fits situations like: user has capital and wants end-to-end LP management; tasks that involve Multi-agent orchestration; tasks that involve End-to-end testing.

How do I install Full Lp Workflow in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a claude-code`. Or copy the skill folder (skills/full-lp-workflow in LeoYeAI/openclaw-master-skills) into .claude/skills/full-lp-workflow in your project. Claude Code loads it when a task matches its description.

How do I install Full Lp Workflow in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a codex`. Or copy the skill folder (skills/full-lp-workflow in LeoYeAI/openclaw-master-skills) into .agents/skills/full-lp-workflow in your project. Codex loads it when a task matches its description.

Can I use Full Lp Workflow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/full-lp-workflow, .gemini/skills/full-lp-workflow, .github/skills/full-lp-workflow and .opencode/skills/full-lp-workflow in your project.

What does Full Lp Workflow need to run?

SKILL.md names no scripts, command-line tools or credentials: Full Lp Workflow is instructions for the agent only. Its frontmatter pre-approves these tools: Task(subagent_type:opportunity-scanner), Task(subagent_type:lp-strategist), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), Task(subagent_type:liquidity-manager), Task(subagent_type:portfolio-analyst), mcp__uniswap__check_safety_status, mcp__uniswap__get_agent_balance.

Does Full Lp Workflow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Full Lp Workflow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Full Lp Workflow use?

Full Lp Workflow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Full Lp Workflow use?

About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Full Lp Workflow?

Skills that share tags, products or a category with Full Lp Workflow: Orca CLI (stablyai/orca, 88k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Full Lp Workflow?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.