Agent skill

Research And Trade

by LeoYeAI in LeoYeAI/openclaw-master-skills

Research a token and execute a trade if it passes due diligence.

MITAuto-check passedBusiness, Finance & HR

Install Research And Trade

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill research-and-trade -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills research-and-trade --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/research-and-trade .claude/skills/research-and-trade && 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
research-and-trade
GitHub stars
2.2k
Token cost
~4.2k tokens
SKILL.md length
950 words
Files
3
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Research a token and execute a trade if it passes due diligence.

  • Works in 5 steps: Research (token-analyst) → Pool Analysis (pool-researcher) → Risk Assessment (risk-assessor) → …
  • User wants research X and buy if it looks good
  • SKILL.md covers Overview, When to Use, Parameters and Workflow, plus 3 more sections
  • Reaches etherscan.io

What it does

Research And Trade is an agent skill from LeoYeAI/openclaw-master-skills. Research a token and execute a trade if it passes due diligence. Autonomous research-to-trade pipeline: researches the token, evaluates risk, and only trades if the risk assessment approves. Stops and reports if risk is too high. Use when user wants "research X and buy if it looks good" or "due diligence then trade."

Its SKILL.md is about 4.2k 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 Business, Finance & HR, covering Fundraising and pitch decks. 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 wants research X and buy if it looks good
  • Due diligence then trade

Example prompts

  • “research X and buy if it looks good”
  • “due diligence then trade.”
  • “/research-and-trade”

Requirements

  • Pre-approved tools (allowed-tools): Task(subagent_type:token-analyst), Task(subagent_type:pool-researcher), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), mcp__uniswap__check_safety_status

Workflow steps

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

  1. Research (token-analyst)
  2. Pool Analysis (pool-researcher)
  3. Risk Assessment (risk-assessor)
  4. User Confirmation
  5. Execute (trade-executor)

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:token-analyst)
    • Task(subagent_type:pool-researcher)
    • Task(subagent_type:risk-assessor)
    • Task(subagent_type:trade-executor)
    • mcp__uniswap__check_safety_status

    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

    Hosts in commands or code, which the agent is likely to contact:

    • etherscan.io

    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

Research And Trade loads about 4.2k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 950 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

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). 950 words, ~4,206 tokens.

Download SKILL.mdSave it as .claude/skills/research-and-trade/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
research-and-trade
description
Research a token and execute a trade if it passes due diligence. Autonomous research-to-trade pipeline: researches the token, evaluates risk, and only trades if the risk assessment approves. Stops and reports if risk is too high. Use when user wants "research X and buy if it looks good" or "due diligence then trade."
allowed-tools
Task(subagent_type:token-analyst), Task(subagent_type:pool-researcher), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), mcp__uniswap__check_safety_status
model
opus

Research and Trade

Overview

This is the autonomous research-to-execution pipeline. Instead of manually calling four different agents and wiring their outputs together, this skill runs the full expert workflow in one command: research a token, find the best pool, assess risk, and -- only if the risk assessment approves -- execute the trade.

Why this is 10x better than calling agents individually:

  1. Compound context: Each agent receives the accumulated findings from all prior agents. The risk-assessor doesn't just evaluate a swap in isolation -- it sees the token-analyst's liquidity warnings, the pool-researcher's depth analysis, and the exact trade size, enabling a far richer risk assessment than standalone invocation.
  2. Automatic risk gating: A VETO at any stage short-circuits the pipeline immediately. No wasted gas, no wasted time, and you get a full explanation of why.
  3. Single command for a 4-step expert workflow: Manually coordinating token research, pool selection, risk evaluation, and trade execution takes significant time and expertise. This compresses it into one natural-language request.
  4. Progressive disclosure: You see each stage's findings as they complete, not just a final result. If the pipeline stops at risk assessment, you still get the full research report.

When to Use

Activate when the user says anything like:

  • "Research UNI and buy if it looks good"
  • "Due diligence on AAVE then trade"
  • "Investigate and trade ARB"
  • "Should I buy LINK? If so, do it"
  • "Is PEPE safe to trade? Buy $500 worth if yes"
  • "Research X, assess risk, and swap if it passes"
  • "Autonomous trade: research then execute"
  • "Check out TOKEN and buy some if the risk is acceptable"

Do NOT use when the user just wants research without trading (use research-token instead) or just wants to execute a swap without research (use execute-swap instead).

Parameters

ParameterRequiredDefaultHow to Extract
tokenYes--Token to research and potentially buy: "UNI", "AAVE", or 0x addr
amountYes--Trade size: "$500", "1 ETH worth", "0.5 ETH"
chainNoethereumTarget chain: "ethereum", "base", "arbitrum"
riskToleranceNomoderate"conservative", "moderate", "aggressive"
actionNobuy"buy" (swap into token) or "sell" (swap out of token)
payWithNoWETHToken to spend: "WETH", "USDC", etc.

If the user doesn't provide an amount, ask for it -- never guess a trade size.

Workflow

                          RESEARCH-AND-TRADE PIPELINE
  ┌─────────────────────────────────────────────────────────────────────┐
  │                                                                     │
  │  Step 1: RESEARCH (token-analyst)                                   │
  │  ├── Token metadata, liquidity, volume, risk factors                │
  │  ├── Cross-chain presence                                           │
  │  └── Output: Token Research Report                                  │
  │          │                                                          │
  │          ▼ (feeds into Step 2)                                      │
  │                                                                     │
  │  Step 2: POOL ANALYSIS (pool-researcher)                            │
  │  ├── Find all pools for {token}/{payWith} on {chain}                │
  │  ├── Rank by fee APY, depth, utilization                            │
  │  ├── Analyze depth at trade size (can it handle $X?)                │
  │  └── Output: Pool Research Report + Best Pool Selection             │
  │          │                                                          │
  │          ▼ (feeds into Step 3 with COMPOUND CONTEXT)                │
  │                                                                     │
  │  Step 3: RISK ASSESSMENT (risk-assessor)                            │
  │  ├── Receives: token risks + pool risks + trade size + slippage     │
  │  ├── Evaluates: slippage, liquidity, smart contract, token risk     │
  │  ├── Decision: APPROVE / CONDITIONAL_APPROVE / VETO / HARD_VETO    │
  │  └── Output: Risk Assessment Report                                 │
  │          │                                                          │
  │          ▼ CONDITIONAL GATE                                         │
  │  ┌───────────────────────────────────────────────────┐              │
  │  │  APPROVE         → Proceed to Step 4              │              │
  │  │  COND. APPROVE   → Show conditions, ask user      │              │
  │  │  VETO            → STOP. Show research + reason.  │              │
  │  │  HARD VETO       → STOP. Non-negotiable.          │              │
  │  └───────────────────────────────────────────────────┘              │
  │          │ (only if APPROVE or user confirms CONDITIONAL)           │
  │          ▼                                                          │
  │                                                                     │
  │  Step 4: USER CONFIRMATION                                          │
  │  ├── Present: research summary + risk score + swap quote            │
  │  ├── Ask: "Proceed with this trade?"                                │
  │  └── User must explicitly confirm                                   │
  │          │                                                          │
  │          ▼                                                          │
  │                                                                     │
  │  Step 5: EXECUTE (trade-executor)                                   │
  │  ├── Execute swap through safety-guardian pipeline                  │
  │  ├── Monitor transaction confirmation                               │
  │  └── Output: Trade Execution Report                                 │
  │                                                                     │
  └─────────────────────────────────────────────────────────────────────┘
Step 1: Research (token-analyst)

Delegate to Task(subagent_type:token-analyst) with:

  • Token symbol or address
  • Target chain
  • Request: full due diligence report

What to pass to the agent:

Research this token for a potential trade:
- Token: {token}
- Chain: {chain}
- Trade size: {amount}

Provide a full due diligence report: liquidity across all pools, volume profile
(24h/7d/30d), risk factors, and a trading recommendation with maximum trade size
at < 1% price impact.

Present to user after completion:

text
Step 1/5: Token Research Complete

  Token: UNI (Uniswap) on Ethereum
  Total Liquidity: $85M across 24 pools
  24h Volume: $15M | 7d Volume: $95M
  Volume Trend: Stable
  Risk Factors: None significant
  Max Trade (< 1% impact): $2.5M

  Proceeding to pool analysis...

Gate check: If the token-analyst reports critical risk factors (total liquidity < $100K, no pools found, token not verified), present findings and ask the user if they want to continue before proceeding.

Step 2: Pool Analysis (pool-researcher)

Delegate to Task(subagent_type:pool-researcher) with the token research output:

Find the best pool for trading {token}/{payWith} on {chain}.

Context from token research:
- Total liquidity: {from Step 1}
- Dominant pool: {from Step 1}
- Risk factors: {from Step 1}

Trade details:
- Trade size: {amount}
- Direction: {action} (buying/selling {token})

Analyze all pools for this pair across fee tiers. For each pool, report:
fee APY, TVL, liquidity depth at the trade size, and price impact estimate.
Recommend the best pool for this specific trade.

Present to user after completion:

text
Step 2/5: Pool Analysis Complete

  Best Pool: WETH/UNI 0.3% (V3, Ethereum)
  Pool TVL: $42M
  Price Impact: ~0.3% for your trade size
  Fee Tier: 0.3% (3000 bps)

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

Delegate to Task(subagent_type:risk-assessor) with compound context from Steps 1 and 2:

Evaluate risk for this proposed swap:

Operation: swap {amount} {payWith} for {token}
Pool: {best pool from Step 2}
Chain: {chain}
Risk tolerance: {riskTolerance}

Token research context (from token-analyst):
{Full token research summary from Step 1}

Pool analysis context (from pool-researcher):
{Full pool analysis from Step 2}

Evaluate all applicable risk dimensions: slippage, liquidity, smart contract risk.
Provide a clear APPROVE / CONDITIONAL_APPROVE / VETO / HARD_VETO decision.

Conditional gate logic after risk-assessor returns:

DecisionAction
APPROVEPresent risk summary, proceed to Step 4 (user confirmation)
CONDITIONAL_APPROVEShow conditions (e.g., "split into 2 tranches"). Ask user: "Accept conditions?"
VETOSTOP. Show full research report + risk assessment + veto reason. Suggest alternatives.
HARD_VETOSTOP. Show reason. Non-negotiable -- do not offer to proceed.

Present to user (APPROVE case):

text
Step 3/5: Risk Assessment Complete

  Decision: APPROVE
  Composite Risk: LOW
  Slippage Risk: LOW (0.3% estimated)
  Liquidity Risk: LOW (pool TVL 840x trade size)
  Smart Contract Risk: LOW (V3, 18-month-old pool)

  Ready for your confirmation...

Present to user (VETO case):

text
Step 3/5: Risk Assessment -- VETOED

  Decision: VETO
  Reason: Price impact of 4.2% exceeds moderate risk tolerance (max 2%)

  Research Summary:
    Token: SMALLCAP ($180K total liquidity)
    Best Pool: WETH/SMALLCAP 1% (V3, $95K TVL)
    Your trade size ($5,000) represents 5.3% of pool TVL

  Suggestions:
    - Reduce trade size to < $1,000 for acceptable slippage
    - Use a limit order instead: "Submit limit order for SMALLCAP"
    - Try a different chain if more liquidity exists elsewhere

  Pipeline stopped. No trade executed.
Step 4: User Confirmation

Before executing any trade, present a clear summary and ask for explicit confirmation:

text
Trade Confirmation Required

  Research: UNI — $85M liquidity, stable volume, no risk factors
  Risk: APPROVED (LOW composite risk)

  Swap Details:
    Sell:  0.5 WETH (~$980)
    Buy:   ~28.5 UNI
    Pool:  WETH/UNI 0.3% (V3, Ethereum)
    Impact: ~0.3%
    Gas:   ~$8 estimated

  Proceed with this trade? (yes/no)

Only proceed to Step 5 if the user explicitly confirms.

Step 5: Execute (trade-executor)

Delegate to Task(subagent_type:trade-executor) with the full pipeline context:

Execute this swap:
- Sell: {amount} {payWith}
- Buy: {token}
- Pool: {best pool address from Step 2}
- Chain: {chain}
- Slippage tolerance: {derived from risk assessment}
- Risk assessment: APPROVED, composite risk {level}

The token has been researched (liquidity: {X}, volume: {Y}) and risk-assessed
(slippage: {Z}, liquidity: {W}). Proceed with execution through the safety pipeline.

Present final result:

text
Step 5/5: Trade Executed

  Sold:     0.5 WETH ($980.00)
  Received: 28.72 UNI ($985.50)
  Pool:     WETH/UNI 0.3% (V3, Ethereum)
  Slippage: 0.28% (within tolerance)
  Gas:      $7.20
  Tx:       https://etherscan.io/tx/0x...

  ──────────────────────────────────────
  Pipeline Summary
  ──────────────────────────────────────
  Research:  UNI — $85M liquidity, stable, no risk flags
  Pool:      WETH/UNI 0.3% — best depth for trade size
  Risk:      APPROVED (LOW)
  Execution: Success — 28.72 UNI received
  Total cost: $987.20 (trade + gas)
Show full SKILL.md (378 more words)Show less

Output Format

Successful Pipeline (all 5 steps)
text
Research and Trade Complete

  Token: {symbol} ({name}) on {chain}
  Research: {1-line summary from token-analyst}
  Pool: {pool pair} {fee}% ({version}, {chain})
  Risk: {decision} ({composite_risk})

  Trade:
    Sold:     {amount} {payWith} (${usd_value})
    Received: {amount} {token} (${usd_value})
    Impact:   {slippage}%
    Gas:      ${gas_cost}
    Tx:       {explorer_link}

  Pipeline: Research -> Pool -> Risk -> Confirm -> Execute (all passed)
Vetoed Pipeline (stopped at risk)
text
Research and Trade -- Risk Vetoed

  Token: {symbol} ({name}) on {chain}
  Research: {1-line summary}
  Pool: {best pool found}
  Risk: VETOED — {reason}

  Details:
    {risk dimension scores}

  Suggestions:
    - {mitigation 1}
    - {mitigation 2}

  Pipeline: Research -> Pool -> Risk (VETOED) -- No trade executed.

Important Notes

  • This skill always researches first. It never skips to trading. If the user just wants a quick swap without research, redirect them to execute-swap.
  • Risk gating is non-negotiable for HARD_VETO. If the risk-assessor issues a HARD_VETO (unverified token, pool TVL < $1K, price impact > 10%), the pipeline stops. The user cannot override this.
  • VETO is informational. For a regular VETO, present the full research and explain why. The user can then choose to use execute-swap directly if they want to proceed at their own risk -- but this skill will not do it.
  • Compound context is the key differentiator. The risk-assessor is dramatically more useful when it has the token-analyst's risk factors and the pool-researcher's depth analysis, compared to calling it standalone with just a swap request.
  • Progressive output keeps the user informed. Don't wait until the end to show results. After each agent completes, show a brief summary so the user knows what's happening.
  • Amount is required. Never assume a trade size. If the user says "research and buy UNI" without an amount, ask: "How much would you like to trade?"

Error Handling

ErrorUser-Facing MessageSuggested Action
Token not found"Could not find token {X} on {chain}."Check spelling or provide contract address
No pools found"No Uniswap pools found for {token}/{payWith} on {chain}."Try different pay token or chain
Token-analyst fails"Token research failed: {reason}. Cannot proceed without due diligence."Try again or use research-token directly
Pool-researcher fails"Pool analysis failed. Research completed but cannot find optimal pool."Try execute-swap with manual pool choice
Risk-assessor VETO"Risk assessment vetoed this trade: {reason}."Reduce amount, try different token/pool
Risk-assessor HARD_VETO"Trade blocked: {reason}. This cannot be overridden."The trade is unsafe at any size
Trade-executor fails"Trade execution failed: {reason}. Research and risk data preserved."Check wallet, balance, gas; retry
Safety check fails"Safety limits exceeded. Check spending limits with check-safety."Wait for limit reset or adjust limits
User declines confirmation"Trade cancelled. Research and risk data are shown above for reference."No action needed
Wallet not configured"No wallet configured. Cannot execute trades."Set up wallet with setup-agent-wallet
Insufficient balance"Insufficient {payWith} balance: have {X}, need {Y}."Reduce amount or acquire more tokens

© 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/research-and-trade of LeoYeAI/openclaw-master-skills.

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

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Research And Trade 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.

Research And Trade compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Research And Trade this skillLeoYeAI/openclaw-master-skills2.2k—~4.2kAutomated safety check: PassMIT
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Yc Applypedronauck/skills634—~1.3kAutomated safety check: PassNone
Storyline Buildersruthir28/enterprise-ai-skills1481 repos~1.9kAutomated safety check: PassMIT
Startup Pitchferdinandobons/startup-skill1.2k—~6.4kAutomated safety check: PassMIT
Dd SourcingAbilityai/trinity636—~664Automated safety check: PassApache-2.0

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Questions about Research And Trade

What does Research And Trade do?

Research a token and execute a trade if it passes due diligence. Research And Trade is an agent skill from LeoYeAI/openclaw-master-skills. Research a token and execute a trade if it passes due diligence.

When should I use Research And Trade?

Research And Trade fits situations like: user wants research X and buy if it looks good; due diligence then trade.

How do I install Research And Trade in Claude Code?

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

How do I install Research And Trade in Codex?

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

Can I use Research And Trade 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 research-and-trade -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-and-trade, .gemini/skills/research-and-trade, .github/skills/research-and-trade and .opencode/skills/research-and-trade in your project.

What does Research And Trade need to run?

SKILL.md names no scripts, command-line tools or credentials: Research And Trade is instructions for the agent only. Its frontmatter pre-approves these tools: Task(subagent_type:token-analyst), Task(subagent_type:pool-researcher), Task(subagent_type:risk-assessor), Task(subagent_type:trade-executor), mcp__uniswap__check_safety_status.

Does Research And Trade access the network?

SKILL.md names 1 domain. In commands or code: etherscan.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Research And Trade 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 Research And Trade use?

Research And Trade 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 Research And Trade use?

About 4.2k tokens (SKILL.md is roughly 17k 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 Research And Trade?

Skills that share tags, products or a category with Research And Trade: Virtuals Protocol Acp (Virtual-Protocol/openclaw-acp, 168 stars), Yc Apply (pedronauck/skills, 634 stars), Storyline Builder (sruthir28/enterprise-ai-skills, 148 stars) and Startup Pitch (ferdinandobons/startup-skill, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research And Trade?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 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.