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Full LP workflow from opportunity scanning to position entry.
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .claude/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/full-lp-workflow .agents/skills/full-lp-workflow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .agents/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/full-lp-workflow .cursor/skills/full-lp-workflow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .cursor/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/full-lp-workflow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/full-lp-workflow .gemini/skills/full-lp-workflow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .gemini/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/full-lp-workflow .github/skills/full-lp-workflow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .github/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill full-lp-workflow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills full-lp-workflow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/full-lp-workflow .opencode/skills/full-lp-workflow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "full-lp-workflow" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/full-lp-workflow into .opencode/skills/full-lp-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "full-lp-workflow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
full-lp-workflowFull 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. 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.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
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_statusmcp__uniswap__get_agent_balanceFrom allowed-tools in the SKILL.md frontmatter.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,208 words, ~5,796 tokens.
.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.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:
Activate when the user says anything like:
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).
| Parameter | Required | Default | How to Extract |
|---|---|---|---|
| capital | Yes | -- | Total capital to deploy: "$20,000", "10 ETH", "$5K" |
| chain | No | all | Target chain or "all" for cross-chain scan |
| riskTolerance | No | moderate | "conservative", "moderate", "aggressive" |
| pairPreference | No | -- | Optional token pair preference: "ETH/USDC", "stablecoin pairs" |
| excludeTokens | No | -- | Tokens to exclude: "PEPE, SHIB" (avoid memecoins, etc.) |
| capitalToken | No | auto-detect | What 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.
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 │
│ │
└─────────────────────────────────────────────────────────────────────┘Before starting the pipeline:
mcp__uniswap__check_safety_status -- verify spending limits can accommodate the capital amount.mcp__uniswap__get_agent_balance on the target chain(s) -- verify the wallet has the stated capital.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:
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.
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 alternativePresent to user:
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...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):
| Decision | Action |
|---|---|
| APPROVE | Present risk summary, proceed to Step 4 |
| CONDITIONAL_APPROVE | Show conditions, ask user if they accept |
| VETO | STOP. Show opportunity + strategy + veto reason. Suggest fallbacks. |
| HARD_VETO | STOP. Show reason. Non-negotiable. |
Present to user (APPROVE case):
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...Check wallet balances against required token amounts for the LP position:
mcp__uniswap__get_agent_balance.If no swaps needed:
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:
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).
Present the full LP entry details and ask USER CONFIRMATION #2:
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.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 recommendationsPresent final result:
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"These are the moments where the skill must stop and ask rather than assume:
| Situation | Action |
|---|---|
| Capital amount not specified | Ask: "How much capital would you like to deploy?" |
| Multiple good opportunities | Present top 3-5, let user choose |
| Risk-assessor VETO | Stop. Show research + strategy + veto reason. |
| Swap needed for token preparation | USER CONFIRMATION #1: show swap details, ask to proceed |
| Ready to add liquidity | USER CONFIRMATION #2: show position details, ask to proceed |
| Capital exceeds safety spending limit | Stop. Show limit. Suggest reducing amount or adjusting limits. |
| Wallet doesn't have stated capital | Stop. Show actual balance. Ask to adjust amount. |
| Best opportunity is on a different chain than wallet funds | Explain cross-chain situation, suggest bridge or chain choice |
If the pipeline fails mid-way, report what was accomplished and what remains:
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"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)| Error | User-Facing Message | Suggested 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
SKILL.md and 2 other files in skills/full-lp-workflow of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Full Lp Workflow this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~5.8k | Automated safety check: Pass | MIT | |
| Orca CLIstablyai/orca | 88k | 2 repos | ~593 | Automated safety check: Pass | MIT | |
| Paseo Advisor Second Opiniongetpaseo/paseo | 20k | 1 repos | ~756 | Automated safety check: Pass | Custom licence | |
| O2 Review Loopopenobserve/openobserve | 22k | — | ~3.7k | Automated safety check: Pass | AGPL-3.0 | |
| Paseo Committeegetpaseo/paseo | 20k | 1 repos | ~496 | Automated safety check: Pass | Custom licence | |
| Mission Control Agent APIbuilderz-labs/mission-control | 6.3k | — | ~2.1k | Automated safety check: Pass | MIT |
stablyai/orca
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…
getpaseo/paseo
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.