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Agent skill

Trading Agents

by LeoYeAI in LeoYeAI/openclaw-master-skills

Multi-agent stock trading signal analysis framework with two-round debate mechanism.

MITAuto-check passedAgent Workflows

Install Trading Agents

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill trading-agents -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills trading-agents --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/trading-agents-simplified .claude/skills/trading-agents && 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
trading-agents
GitHub stars
2.2k
Token cost
~4.2k tokens
SKILL.md length
1,205 words
Files
12 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Multi-agent stock trading signal analysis framework with two-round debate mechanism.

  • Works in 7 steps: Export to Markdown Report → Parse Stock Ticker → Execute Layer 1 SubAgents in Parallel → …
  • Phrases: analyze this stock
  • SKILL.md covers Environment Requirements, Security Rules, Workflow (4-Layer Architecture) and Execution Steps, plus 2 more sections
  • Runs Python scripts from its folder; calls python3 and pip; needs TUSHARE_TOKEN and BRAVE_API_KEY

What it does

Trading Agents is an agent skill from LeoYeAI/openclaw-master-skills. Multi-agent stock trading signal analysis framework with two-round debate mechanism. Triggered when users provide a stock ticker for investment analysis. Input a stock ticker, analyze through 7 SubAgents in 4 layers (Information Gathering → Opinion Formation → Two-Round Debate → Final Decision), output BUY/SELL/HOLD recommendation with rationale. Trigger phrases: "analyze this stock", "give me investment advice", "is this stock worth buying", "analyze XX stock", "stock investment analysis".

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts and reference files (for example `_meta.json`, `references/bear-researcher.md` and `references/bull-researcher.md`).

It sits in Agent Workflows, covering Trading and backtesting and Subagents. 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

  • Phrases: analyze this stock
  • Give me investment advice
  • Is this stock worth buying
  • Analyze XX stock

Example prompts

  • “analyze this stock”
  • “give me investment advice”
  • “is this stock worth buying”
  • “/trading-agents”

Requirements

  • Python 3
  • A credential in TUSHARE_TOKEN
  • A credential in BRAVE_API_KEY

Workflow steps

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

  1. Export to Markdown Report
  2. Parse Stock Ticker
  3. Execute Layer 1 SubAgents in Parallel
  4. Collect Layer 1 Reports
  5. Execute Layer 2 SubAgents in Parallel (Initial Reports)
  6. Two-Round Debate
  7. Execute Final Decision SubAgent

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 nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

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

  • Network

    Links to these hosts (documentation or services it may open):

    • tushare.pro

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • TUSHARE_TOKEN
    • BRAVE_API_KEY

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

Context cost

Trading Agents loads about 4.2k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 128 tokens; SKILL.md has 1,205 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~128
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,205 words, ~4,165 tokens.

Download SKILL.mdSave it as .claude/skills/trading-agents/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
trading-agents
description
Multi-agent stock trading signal analysis framework with two-round debate mechanism. Triggered when users provide a stock ticker for investment analysis. Input a stock ticker, analyze through 7 SubAgents in 4 layers (Information Gathering → Opinion Formation → Two-Round Debate → Final Decision), output BUY/SELL/HOLD recommendation with rationale. Trigger phrases: "analyze this stock", "give me investment advice", "is this stock worth buying", "analyze XX stock", "stock investment analysis".
license
MIT
author
laigen
requires
env: - name: TUSHARE_TOKEN required: true - name: BRAVE_API_KEY required: false pip: - tushare>=1.3.0 - pandas>=1.5.0 - numpy>=1.21.0

TradingAgents - Stock Trading Signal Analysis Assistant

Multi-agent collaborative stock trading signal analysis framework with two-round debate mechanism, inspired by the open-source TradingAgents project.

Environment Requirements

Required Environment Variables
VariableRequiredDescription
TUSHARE_TOKEN✅Tushare Pro API Token, get at: https://tushare.pro/register
BRAVE_API_KEY⚪Brave Search API Key (optional, for news search enhancement)
Python Dependencies

Install required packages before using this skill:

bash
pip install tushare>=1.3.0 pandas>=1.5.0 numpy>=1.21.0
Secure Configuration

Security Warning: Do NOT paste your API tokens in chat messages.

Set Environment Variables

Before running this skill, ensure the following environment variables are set:

bash
export TUSHARE_TOKEN=your_token_here
export BRAVE_API_KEY=your_brave_key_here  # optional

Avoid:

  • Do NOT add tokens to ~/.bashrc, ~/.zshrc, or other shell config files
  • Do NOT include tokens in reports or SubAgent communications
  • Do NOT paste tokens in chat messages
Side Effects

This skill will:

  • Write reports to ~/.openclaw/workspace/memory/reports/trading-agents-*.md
  • Connect to Tushare Pro API (api.tushare.pro)
  • Use web_search for news and sentiment data

No data is transmitted externally beyond these API calls.

Security Rules

Credential Protection
  1. Only read TUSHARE_TOKEN and BRAVE_API_KEY from environment variables
  2. Never read other configuration files (e.g., openclaw.json, .bashrc, etc.)
  3. Never include API keys or tokens in:
    • SubAgent inter-communications
    • Generated reports
    • Any output files
Report Content Rules

All generated reports and SubAgent communications MUST NOT contain:

  • API keys (TUSHARE_TOKEN, BRAVE_API_KEY)
  • Passwords or secrets
  • Any credential values

If a SubAgent receives or generates content containing sensitive credentials, it must redact them before passing to other agents or writing to files.

Workflow (4-Layer Architecture)

Input: Stock Ticker (e.g., 300750.SZ)
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 1: Information Gathering (Parallel)          │
│  ├─ SubAgent 1: Fundamental Analyst                 │
│  ├─ SubAgent 2: Market Analyst                      │
│  ├─ SubAgent 3: News Analyst                        │
│  └─ SubAgent 4: Social Media Analyst                │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 2: Opinion Formation (Parallel)              │
│  ├─ SubAgent 5: Bull Researcher (Initial Report)    │
│  └─ SubAgent 6: Bear Researcher (Initial Report)    │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 2.5: Two-Round Debate (Sequential)           │
│  ├─ Round 1:                                        │
│  │   ├─ Bear Researcher → Bull's Arguments          │
│  │   └─ Bull Researcher → Bear's Arguments          │
│  └─ Round 2:                                        │
│      ├─ Bear Researcher → Bull's Rebuttals          │
│      └─ Bull Researcher → Bear's Rebuttals          │
└─────────────────────────────────────────────────────┘
  ↓
┌─────────────────────────────────────────────────────┐
│  Layer 3: Final Decision                            │
│  └─ SubAgent 7: Research Manager                    │
│      (Synthesizes all reports + debate history)     │
└─────────────────────────────────────────────────────┘
  ↓
Output: BUY/SELL/HOLD + Investment Plan (Markdown)
Step 7: Export to Markdown Report

Research Manager outputs the complete report to a markdown file:

~/.openclaw/workspace/memory/reports/trading-agents-[stock_code]-[timestamp].md

The markdown file contains:

  • Part I: Final Investment Decision
  • Part II: Layer 1 Reports (4 reports)
  • Part III: Layer 2 Reports (2 initial reports)
  • Part IV: Debate History (4 responses)
  • Part V: Appendix

Execution Steps

Step 1: Parse Stock Ticker

Extract stock ticker from user input, format to standard:

  • A-shares: 600519.SH, 300750.SZ
  • HK stocks: 00700.HK
  • US stocks: AAPL
Step 2: Execute Layer 1 SubAgents in Parallel

Use sessions_spawn to launch in parallel 4 SubAgents:

SubAgentTaskOutput
Fundamental AnalystFundamental analysisFundamental report
Market AnalystMarket technical analysisMarket analysis report
News AnalystNews analysisNews summary report
Social Media AnalystSocial sentiment analysisSentiment report
Step 3: Collect Layer 1 Reports

Use subagents(action=list) to check all SubAgent completion status, then collect report content.

Step 4: Execute Layer 2 SubAgents in Parallel (Initial Reports)

Pass Layer 1 reports to Layer 2, launch in parallel 2 SubAgents:

SubAgentTaskInputOutput
Bull ResearcherInitial bull reportLayer 1 4 reportsBull report (initial)
Bear ResearcherInitial bear reportLayer 1 4 reportsBear report (initial)
Step 5: Two-Round Debate

After Layer 2 completes, initiate two rounds of debate:

Round 1:
  1. Bear Researcher receives Bull's initial report → Refutes bull arguments
  2. Bull Researcher receives Bear's initial report → Refutes bear arguments
Round 2:
  1. Bear Researcher receives Bull's Round 1 rebuttals → Counter-rebuttal
  2. Bull Researcher receives Bear's Round 1 rebuttals → Counter-rebuttal

Debate Rules:

  • Use specific data and reasoning to refute opponent's arguments
  • Cite sources from all available reports
  • Apply conversational debate style
  • Reflect on past experience and lessons learned
  • Each response should directly address opponent's specific points
  • Never include API keys or tokens in debate content
Step 6: Execute Final Decision SubAgent

Launch Research Manager with ALL inputs:

  • Layer 1: 4 reports (Fundamental, Market, News, Social)
  • Layer 2: 2 initial reports (Bull, Bear)
  • Layer 2.5: 4 debate responses (Round 1 + Round 2)

Research Manager synthesizes all information and makes final investment recommendation.

SubAgent Details

SubAgent 1: Fundamental Analyst

Data Sources:

  • Tushare Pro API (financial data, valuation metrics)
  • Company annual/quarterly reports

Analysis Dimensions:

  1. Financial statement analysis
  2. Valuation metrics (PE/PB/PS percentile)
  3. Growth metrics
  4. Company basic information
  5. Shareholder structure

Detailed Prompt: See references/fundamental-analyst.md

SubAgent 2: Market Analyst

Data Sources:

  • Tushare Pro API (daily data)
  • Technical indicator calculations

Analysis Dimensions:

  1. Price trend (MA system)
  2. Technical indicators (MACD, RSI, KDJ, BOLL)
  3. Volume analysis
  4. Capital flow
  5. Market anomaly signals

Detailed Prompt: See references/market-analyst.md

SubAgent 3: News Analyst

Data Sources:

  • Web Search (Brave Search API)
  • Financial media websites

Analysis Dimensions:

  1. Company news
  2. Industry news
  3. Management dynamics
  4. Macro news

Detailed Prompt: See references/news-analyst.md

SubAgent 4: Social Media Analyst

Data Sources:

  • Web Search
  • Xueqiu, Guba, Weibo, Zhihu, etc.

Analysis Dimensions:

  1. Sentiment heat
  2. Investor sentiment (bullish/bearish ratio)
  3. Controversy focus
  4. KOL opinions

Detailed Prompt: See references/social-analyst.md

Show full SKILL.md (487 more words)Show less
SubAgent 5: Bull Researcher

Role: Bull analyst advocating for investment

Tasks:

  1. Generate initial bull report from Layer 1 data
  2. Refute bear's arguments in debate rounds
  3. Provide counter-rebuttals in Round 2

Debate Style:

  • Conversational tone addressing opponent directly
  • Use specific data to counter opponent's points
  • Cite sources from all available reports
  • Reflect on past experience and mistakes
  • Never include API keys in output

Detailed Prompt: See references/bull-researcher.md

SubAgent 6: Bear Researcher

Role: Bear analyst advocating against investment

Tasks:

  1. Generate initial bear report from Layer 1 data
  2. Refute bull's arguments in debate rounds
  3. Provide counter-rebuttals in Round 2

Debate Style:

  • Conversational tone addressing opponent directly
  • Use specific data to expose weaknesses
  • Cite sources from all available reports
  • Reflect on past experience and mistakes
  • Never include API keys in output

Detailed Prompt: See references/bear-researcher.md

SubAgent 7: Research Manager

Role: Portfolio manager making final decision

Input (9 items total):

  1. Fundamental Analysis Report
  2. Market Analysis Report
  3. News Analysis Report
  4. Social Media Sentiment Report
  5. Bull Initial Report
  6. Bear Initial Report
  7. Round 1 Debate (2 responses)
  8. Round 2 Debate (2 responses)

Responsibilities:

  1. Synthesize all reports and debate history
  2. Identify most persuasive arguments from both sides
  3. Make final decision: BUY/SELL/HOLD
  4. Develop detailed investment plan
  5. Ensure final report contains NO API keys or tokens

Important: Do not default to "HOLD" - must take a stance based on strongest arguments.

Detailed Prompt: See references/research-manager.md

Report Format Requirements

Price Change Color Convention (China Style)
  • Red = Up (+)
  • Green = Down (-)
Data Citation
  • All data must cite sources
  • When using Tushare Pro, note Data Source: Tushare Pro
  • When using Web Search, cite source URL
Security Requirement
  • Reports MUST NOT contain any API keys, tokens, or credentials
  • If any credential is found in output, redact immediately
Signature
  • Use signature at end of financial reports

Implementation Scripts

Get Fundamental Data
bash
python3 scripts/get_fundamentals.py <stock_code>
Get Market Data
bash
python3 scripts/get_market_data.py <stock_code>

Output Example

markdown
# TradingAgents Investment Decision Report: 宁德时代 (300750.SZ)

Report Generated: YYYY-MM-DD HH:MM
Framework Version: TradingAgents v2.0 with Debate Mechanism

---

# Part I: Final Investment Decision

## 一、决策摘要

### 投资建议

# 🟢 买入 / 🔴 卖出 / 🟡 持有

### 核心理由

[一句话概括核心理由，基于辩论结果]

### 信心指数

| 维度 | 信心度 | 说明 |
|------|:------:|------|
| 基本面 | x/5 | [说明] |
| 市场面 | x/5 | [说明] |
| 消息面 | x/5 | [说明] |
| 综合信心 | x/5 | - |

---

## 二、辩论精华回顾

### 2.1 初始观点对比

| 方面 | 看涨观点 | 看跌观点 |
|------|----------|----------|
| 核心论点 | [论点] | [论点] |
| 支撑数据 | [数据] | [数据] |
| 初始得分 | x/5 | x/5 |

### 2.2 第一轮辩论结果

#### 看跌方对看涨方的挑战

| 看涨论点 | 看跌方反驳 | 反驳有效性 | 幸存状态 |
|----------|------------|:----------:|:--------:|
| [论点1] | [反驳] | 高/中/低 | ✅/❌ |
| [论点2] | [反驳] | 高/中/低 | ✅/❌ |

#### 看涨方对看跌方的挑战

| 看跌论点 | 看涨方反驳 | 反驳有效性 | 幸存状态 |
|----------|------------|:----------:|:--------:|
| [论点1] | [反驳] | 高/中/低 | ✅/❌ |
| [论点2] | [反驳] | 高/中/低 | ✅/❌ |

### 2.3 第二轮辩论结果

| 议题 | 看涨最终立场 | 看跌最终立场 | 辩论胜出方 |
|------|--------------|--------------|:----------:|
| [议题1] | [立场] | [立场] | 🟢/🔴/🟡 |
| [议题2] | [立场] | [立场] | 🟢/🔴/🟡 |

### 2.4 辩论胜负关键

**看涨方胜出理由** (如适用):
1. [理由1]
2. [理由2]

**看跌方胜出理由** (如适用):
1. [理由1]
2. [理由2]

---

## 三、最终判断

### 3.1 为什么选择 [买入/卖出/持有]

[详细解释基于辩论结果做出此决策的原因]

### 3.2 经过辩论验证的核心论点

#### 买入/持有支撑论点 (经辩论验证)

| 优先级 | 论点 | 辩论验证结果 | 来源 |
|:------:|------|--------------|------|
| 1 | [论点] | 看跌方无法有效反驳 | [来源] |
| 2 | [论点] | 看跌方反驳力度不足 | [来源] |

#### 卖出/回避风险论点 (经辩论验证)

| 优先级 | 论点 | 辩论验证结果 | 来源 |
|:------:|------|--------------|------|
| 1 | [论点] | 看涨方无法有效反驳 | [来源] |
| 2 | [论点] | 风险确认 | [来源] |

### 3.3 辩论暴露的关键风险

| 风险 | 暴露程度 | 应对策略 |
|------|:--------:|----------|
| [风险1] | 高/中/低 | [策略] |
| [风险2] | 高/中/低 | [策略] |

---

## 四、投资计划

### a) 投资建议

**[买入/卖出/持有]**

| 配置建议 | 建议 |
|----------|------|
| 建议仓位 | 低配(10-20%) / 标配(20-40%) / 高配(40-60%) |
| 持有期限 | 短线(1-3月) / 中线(3-12月) / 长线(1年+) |
| 信心等级 | 低 / 中 / 高 |

### b) 理由

[详细解释这些论据为何能够得到该结论]

### c) 战略行动

| 步骤 | 具体操作 | 时间节点 |
|:----:|----------|----------|
| 1 | 分批建仓：建议分x批买入 | [时间] |
| 2 | 入场时机：[技术面建议] | [条件] |
| 3 | 止损设置：设定止损位在 xx | [条件] |

---

## 五、后续跟踪要点

### 需要关注的关键指标

| 指标 | 当前值 | 警戒值 | 触发行动 |
|------|--------|--------|----------|
| [指标1] | xx | xx | [行动] |

### 需要关注的关键事件

| 事件 | 预计时间 | 对论点的影响 | 行动预案 |
|------|---------|--------------|----------|
| [事件1] | [时间] | 影响[论点] | [预案] |

---

## 六、免责声明

本报告基于多智能体辩论分析框架生成，综合了看涨和看跌双方观点及其辩论过程。投资决策应基于个人风险承受能力和投资目标。本报告仅供参考，不构成投资建议。投资有风险，入市需谨慎。

---

# Part II: Layer 1 Reports (Information Gathering)

## 1. Fundamental Analysis Report

[Complete fundamental analyst report]

## 2. Market Analysis Report

[Complete market analyst report]

## 3. News Analysis Report

[Complete news analyst report]

## 4. Social Media Sentiment Report

[Complete social media analyst report]

---

# Part III: Layer 2 Reports (Opinion Formation)

## 5. Bull Researcher Initial Report

[Complete bull researcher initial report]

## 6. Bear Researcher Initial Report

[Complete bear researcher initial report]

---

# Part IV: Debate History (Layer 2.5)

## Round 1

### Bear's Rebuttal to Bull's Arguments

[Bear's round 1 response]

### Bull's Rebuttal to Bear's Arguments

[Bull's round 1 response]

## Round 2

### Bear's Counter-Rebuttal

[Bear's round 2 response]

### Bull's Counter-Rebuttal

[Bull's round 2 response]

---

# Part V: Appendix

## Data Sources

- Tushare Pro API
- Web Search (Brave Search)
- Social Media Platforms

## Methodology

This report was generated using the TradingAgents Multi-Agent Debate Framework:
1. **Layer 1**: 4 specialized analysts gather comprehensive information
2. **Layer 2**: Bull and Bear researchers form opposing viewpoints
3. **Layer 2.5**: Two rounds of structured debate
4. **Layer 3**: Portfolio manager synthesizes debate outcomes into final decision

## Disclaimer

本报告基于多智能体辩论分析框架生成，综合了看涨和看跌双方观点及其辩论过程。投资决策应基于个人风险承受能力和投资目标。本报告仅供参考，不构成投资建议。投资有风险，入市需谨慎。

---

🐂 TradingAgents Multi-Agent Debate Analysis Framework

Version History

VersionDateChanges
v2.4.22026-03-26Synchronized SKILL.md Output Example with research-manager.md Output Format (full Part I-V structure)
v2.4.02026-03-26Enhanced security: removed openclaw.json reference, added SubAgent security rules, unified install spec
v2.3.02026-03-26Removed PDF export, simplified dependencies, unified metadata
v2.2.02026-03-26Security hardening: Fixed metadata inconsistencies, improved credential handling
v2.0.02026-03-25Added two-round debate mechanism
v1.0.02026-03-20Initial release with 7 SubAgents

Security & Privacy

Credential Handling
  • TUSHARE_TOKEN and BRAVE_API_KEY are sensitive credentials
  • Read only from environment variables
  • Do NOT paste tokens in chat messages
  • Do NOT store tokens in shell config files (.bashrc, .zshrc)
Data Handling
  • Financial data fetched from Tushare Pro API only
  • Reports saved locally, not transmitted externally
  • No telemetry or analytics collected
SubAgent Security Rules
  • SubAgents MUST NOT read any configuration beyond TUSHARE_TOKEN and BRAVE_API_KEY
  • SubAgents MUST NOT pass API keys in inter-agent communications
  • Generated reports MUST NOT contain any sensitive credential information
Side Effects
  • Writes reports to ~/.openclaw/workspace/memory/reports/
  • Connects to external APIs (Tushare, web search)
  • Does not modify any other files or configurations

© 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 11 other files (scripts, references) in skills/trading-agents-simplified of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • references/bear-researcher.md
  • references/bull-researcher.md
  • references/fundamental-analyst.md
  • references/market-analyst.md
  • references/news-analyst.md
  • references/research-manager.md
  • references/social-analyst.md
  • scripts/get_fundamentals.py
  • scripts/get_market_data.py
  • skill.json

Open the folder on GitHubat commit e5199b5

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Trading Agents this skillLeoYeAI/openclaw-master-skills2.2k—~4.2kAutomated safety check: PassMIT
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Polymarket Tennislivetennisapi/livetennisapi-mcp152—~3kAutomated safety check: PassMIT
Skill To Cardsundial-org/skills153—~1.5kAutomated safety check: PassNone
Kraken Autonomy Levelskrakenfx/kraken-cli751—~1.3kAutomated safety check: PassMIT
Workflow OrchestrationAnastasiyaW/codex-claude-code-config154—~3.8kAutomated safety check: PassMIT

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Categories

Questions about Trading Agents

What does Trading Agents do?

Multi-agent stock trading signal analysis framework with two-round debate mechanism. Trading Agents is an agent skill from LeoYeAI/openclaw-master-skills. Multi-agent stock trading signal analysis framework with two-round debate mechanism.

When should I use Trading Agents?

Trading Agents fits situations like: phrases: analyze this stock; give me investment advice; is this stock worth buying; analyze XX stock.

How do I install Trading Agents in Claude Code?

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

How do I install Trading Agents in Codex?

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

Can I use Trading Agents 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 trading-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trading-agents, .gemini/skills/trading-agents, .github/skills/trading-agents and .opencode/skills/trading-agents in your project.

What does Trading Agents need to run?

Going by SKILL.md and its folder, Trading Agents needs Python for the scripts in its folder, the command-line tools its instructions call (python3 and pip) and credentials named TUSHARE_TOKEN and BRAVE_API_KEY. Our summary lists: Python 3; A credential in TUSHARE_TOKEN; A credential in BRAVE_API_KEY.

Does Trading Agents access the network?

SKILL.md names 1 domain. As links in the text: tushare.pro. This is read from the text; nothing was executed.

Is Trading Agents 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Trading Agents use?

Trading Agents is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Trading Agents 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. Its references folder adds about 14k tokens, read only when the agent opens those files.

What are the alternatives to Trading Agents?

Skills that share tags, products or a category with Trading Agents: Dr Manhattan (guzus/dr-manhattan, 204 stars), Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars), Skill To Card (sundial-org/skills, 153 stars) and Kraken Autonomy Levels (krakenfx/kraken-cli, 751 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Trading Agents?

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.