Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.
$ npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --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/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto-trader .claude/skills/auto-trader && 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 "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .claude/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-traderType 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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/auto-trader .agents/skills/auto-trader && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .agents/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/auto-trader .cursor/skills/auto-trader && 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 "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .cursor/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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/Signal-Execution-Labs/forex-trading-ai-agent.git --path skills/auto-trader--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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/auto-trader .gemini/skills/auto-trader && 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 "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .gemini/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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 Signal-Execution-Labs/forex-trading-ai-agent auto-traderInstalls 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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/auto-trader .github/skills/auto-trader && 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 "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .github/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/auto-trader .opencode/skills/auto-trader && 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 "auto-trader" agent skill from https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/auto-trader into .opencode/skills/auto-trader/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-trader", 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.
auto-traderAutomated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.
Auto Trader is an agent skill from Signal-Execution-Labs/forex-trading-ai-agent. Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/position_calculator.py`).
It sits in Business, Finance & HR, covering Trading and backtesting. The repository describes itself as: About AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading…. The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit b8a6047. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3From 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.
Auto Trader loads about 2.1k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 180 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); the scripts in this folder are not scanned.
The full file from Signal-Execution-Labs/forex-trading-ai-agent at commit b8a6047, republished under its MIT licence (© Signal-Execution-Labs). 180 words, ~2,086 tokens.
.claude/skills/auto-trader/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Automated trading execution with risk management.
⚠️ WARNING: Automated trading involves significant risk. Always test with small amounts first!
Trading config in ~/.kit/auto-trader.json:
{
"exchange": "binance",
"sandbox": true,
"risk": {
"max_position_pct": 5,
"max_daily_loss_pct": 3,
"default_stop_loss_pct": 2,
"default_take_profit_pct": 4
},
"strategies": ["rsi_reversal", "ma_crossover"],
"symbols": ["BTC/USDT", "ETH/USDT"]
}python3 -c "
account_balance = 10000 # USD
risk_per_trade_pct = 2 # Risk 2% per trade
entry_price = 45000 # BTC entry
stop_loss_price = 44000 # Stop loss
risk_amount = account_balance * (risk_per_trade_pct / 100)
price_risk = entry_price - stop_loss_price
position_size = risk_amount / price_risk
print('📊 POSITION SIZE CALCULATOR')
print('=' * 50)
print(f'Account Balance: \${account_balance:,.2f}')
print(f'Risk per Trade: {risk_per_trade_pct}% (\${risk_amount:,.2f})')
print(f'Entry Price: \${entry_price:,.2f}')
print(f'Stop Loss: \${stop_loss_price:,.2f}')
print(f'Price Risk: \${price_risk:,.2f} per unit')
print()
print(f'✅ Position Size: {position_size:.6f} BTC')
print(f'✅ Position Value: \${position_size * entry_price:,.2f}')
"python3 -c "
import ccxt
import ta
import pandas as pd
# Strategy: Buy when RSI < 30, Sell when RSI > 70
symbol = 'BTC/USDT'
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df['rsi'] = ta.momentum.RSIIndicator(df['close'], 14).rsi()
current_rsi = df['rsi'].iloc[-1]
current_price = df['close'].iloc[-1]
print(f'📊 RSI STRATEGY: {symbol}')
print('=' * 50)
print(f'Price: \${current_price:,.2f}')
print(f'RSI(14): {current_rsi:.1f}')
print()
if current_rsi < 30:
print('🟢 SIGNAL: BUY (RSI oversold)')
print(f' Entry: \${current_price:,.2f}')
print(f' Stop Loss: \${current_price * 0.98:,.2f} (-2%)')
print(f' Take Profit: \${current_price * 1.04:,.2f} (+4%)')
elif current_rsi > 70:
print('🔴 SIGNAL: SELL (RSI overbought)')
else:
print('⚪ NO SIGNAL: RSI in neutral zone (30-70)')
"python3 -c "
import ccxt
import ta
import pandas as pd
symbol = 'BTC/USDT'
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv(symbol, '4h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df['ema_12'] = ta.trend.ema_indicator(df['close'], 12)
df['ema_26'] = ta.trend.ema_indicator(df['close'], 26)
current = df.iloc[-1]
previous = df.iloc[-2]
price = current['close']
print(f'📊 MA CROSSOVER STRATEGY: {symbol}')
print('=' * 50)
print(f'Price: \${price:,.2f}')
print(f'EMA(12): \${current[\"ema_12\"]:,.2f}')
print(f'EMA(26): \${current[\"ema_26\"]:,.2f}')
print()
# Check for crossover
if previous['ema_12'] < previous['ema_26'] and current['ema_12'] > current['ema_26']:
print('🟢 SIGNAL: BUY (Golden Cross - EMA12 crossed above EMA26)')
elif previous['ema_12'] > previous['ema_26'] and current['ema_12'] < current['ema_26']:
print('🔴 SIGNAL: SELL (Death Cross - EMA12 crossed below EMA26)')
elif current['ema_12'] > current['ema_26']:
print('📈 TREND: Bullish (EMA12 > EMA26) - Hold/Look for entries')
else:
print('📉 TREND: Bearish (EMA12 < EMA26) - Stay out or short')
"python3 -c "
import ccxt
# Configuration
EXCHANGE_CONFIG = {'apiKey': 'YOUR_KEY', 'secret': 'YOUR_SECRET', 'sandbox': True}
SYMBOL = 'BTC/USDT'
SIDE = 'buy'
RISK_PCT = 2 # 2% of account
exchange = ccxt.binance(EXCHANGE_CONFIG)
balance = exchange.fetch_balance()
account_value = balance['USDT']['free']
# Calculate position size
ticker = exchange.fetch_ticker(SYMBOL)
price = ticker['last']
risk_amount = account_value * (RISK_PCT / 100)
stop_loss_distance = price * 0.02 # 2% stop
position_size = risk_amount / stop_loss_distance
print(f'📊 EXECUTING TRADE')
print('=' * 50)
print(f'Symbol: {SYMBOL}')
print(f'Side: {SIDE.upper()}')
print(f'Entry: \${price:,.2f}')
print(f'Size: {position_size:.6f}')
print(f'Value: \${position_size * price:,.2f}')
print(f'Stop Loss: \${price * 0.98:,.2f}')
print(f'Take Profit: \${price * 1.04:,.2f}')
print()
# Uncomment to execute
# order = exchange.create_market_buy_order(SYMBOL, position_size)
# print(f'✅ Order executed: {order[\"id\"]}')
print('⚠️ DRY RUN - Uncomment to execute real trade')
"python3 -c "
import ccxt
import time
# Trailing stop: moves stop up as price increases
symbol = 'BTC/USDT'
entry_price = 45000
trailing_pct = 2 # 2% trailing distance
exchange = ccxt.binance()
highest_price = entry_price
stop_price = entry_price * (1 - trailing_pct/100)
print(f'📊 TRAILING STOP: {symbol}')
print(f'Entry: \${entry_price:,.2f}')
print(f'Trailing: {trailing_pct}%')
print('=' * 50)
# Simulation loop
for i in range(10):
ticker = exchange.fetch_ticker(symbol)
current_price = ticker['last']
# Update trailing stop if price moved up
if current_price > highest_price:
highest_price = current_price
stop_price = highest_price * (1 - trailing_pct/100)
pnl_pct = ((current_price - entry_price) / entry_price) * 100
print(f'Price: \${current_price:,.2f} | High: \${highest_price:,.2f} | Stop: \${stop_price:,.2f} | P&L: {pnl_pct:+.2f}%')
if current_price <= stop_price:
print(f'🛑 STOP HIT at \${stop_price:,.2f}')
break
time.sleep(5)
"python3 -c "
# Mock trade data - load from log in practice
trades = [
{'symbol': 'BTC/USDT', 'side': 'buy', 'entry': 45000, 'exit': 46000, 'size': 0.1},
{'symbol': 'ETH/USDT', 'side': 'buy', 'entry': 2500, 'exit': 2450, 'size': 1.0},
{'symbol': 'SOL/USDT', 'side': 'buy', 'entry': 100, 'exit': 108, 'size': 5.0},
]
print('📊 DAILY TRADING REPORT')
print('=' * 50)
total_pnl = 0
wins = 0
losses = 0
for trade in trades:
if trade['side'] == 'buy':
pnl = (trade['exit'] - trade['entry']) * trade['size']
else:
pnl = (trade['entry'] - trade['exit']) * trade['size']
total_pnl += pnl
if pnl >= 0:
wins += 1
else:
losses += 1
emoji = '🟢' if pnl >= 0 else '🔴'
print(f'{emoji} {trade[\"symbol\"]:12} {trade[\"side\"]:4} \${pnl:+,.2f}')
print()
print('=' * 50)
win_rate = (wins / len(trades)) * 100 if trades else 0
print(f'Total Trades: {len(trades)}')
print(f'Win Rate: {win_rate:.0f}% ({wins}W / {losses}L)')
print(f'Total P&L: \${total_pnl:+,.2f}')
"Before enabling auto-trading:
| Rule | Setting |
|---|---|
| Max position size | 5% of account |
| Max daily loss | 3% of account |
| Default stop loss | 2% |
| Default take profit | 4% (2:1 R:R) |
| Max open trades | 3 |
| Type | Use Case |
|---|---|
| Market | Immediate execution |
| Limit | Better price, may not fill |
| Stop Market | Emergency exit |
| Stop Limit | Controlled exit price |
| OCO | Take profit + stop loss together |
All trades logged to ~/.kit/trades/:
{
"id": "trade_001",
"timestamp": "2026-02-09T14:30:00Z",
"symbol": "BTC/USDT",
"side": "buy",
"entry_price": 45000,
"exit_price": 46000,
"size": 0.1,
"pnl": 100,
"strategy": "rsi_reversal"
}© Signal-Execution-Labs, 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 1 other file (scripts) in skills/auto-trader of Signal-Execution-Labs/forex-trading-ai-agent.
Open the folder on GitHubat commit b8a6047
Auto Trader 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 |
|---|---|---|---|---|---|---|
| Auto Trader this skillSignal-Execution-Labs/forex-trading-ai-agent | 162 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Tushare Datazillionare/zillionare | 321 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Digital Oraclekomako-workshop/digital-oracle | 878 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Polyclawchainstacklabs/polyclaw | 359 | 1 repos | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Markdownfacioquo/stock-indicators-dotnet | 1.2k | — | ~812 | Automated safety check: Pass | Apache-2.0 |
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
atilaahmettaner/tradingview-mcp
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…
komako-workshop/digital-oracle
Answer prediction questions using market trading data, not opinions.
chainstacklabs/polyclaw
Trade on Polymarket via split + CLOB execution. An agent skill from chainstacklabs/polyclaw.
facioquo/stock-indicators-dotnet
Format and lint Markdown in this repository against GitHub Flavored Markdown and its markdownlint-cli2 configuration — headers, lists, code fences, callouts (VitePress containers on docs-site pages…
MobiusQuant/OpenMobius-skill
Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave.
Signal-Execution-Labs/forex-trading-ai-agent
Automatic portfolio rebalancing to maintain target allocations.
Signal-Execution-Labs/forex-trading-ai-agent
Set price alerts, volume alerts, indicator alerts (RSI/MACD), and news alerts for crypto trading.
Signal-Execution-Labs/forex-trading-ai-agent
Backtest trading strategies with historical data. An agent skill from Signal-Execution-Labs/forex-trading-ai-agent.
Signal-Execution-Labs/forex-trading-ai-agent
Connect to DeFi protocols for staking, lending, yield farming, and liquidity provision.
Signal-Execution-Labs/forex-trading-ai-agent
Track dividends, manage DRIP (reinvestment), forecast income, and optimize dividend portfolio.
Signal-Execution-Labs/forex-trading-ai-agent
Connect to crypto exchanges (Binance, Kraken, Coinbase, etc.).
Categories
Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit. Auto Trader is an agent skill from Signal-Execution-Labs/forex-trading-ai-agent. Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.
Auto Trader fits situations like: tasks that involve Trading and backtesting.
Run `npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a claude-code`. Or copy the skill folder (skills/auto-trader in Signal-Execution-Labs/forex-trading-ai-agent) into .claude/skills/auto-trader in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a codex`. Or copy the skill folder (skills/auto-trader in Signal-Execution-Labs/forex-trading-ai-agent) into .agents/skills/auto-trader 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 Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto-trader, .gemini/skills/auto-trader, .github/skills/auto-trader and .opencode/skills/auto-trader in your project.
Going by SKILL.md and its folder, Auto Trader needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3; A credential in YOUR_KEY; A credential in YOUR_SECRET.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Auto Trader is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.3k 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 Auto Trader: Tushare Data (zillionare/zillionare, 321 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Digital Oracle (komako-workshop/digital-oracle, 878 stars) and Polyclaw (chainstacklabs/polyclaw, 359 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Signal-Execution-Labs (a GitHub organization) maintains it in Signal-Execution-Labs/forex-trading-ai-agent, which has 162 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 14, 2026.
Source: Signal-Execution-Labs/forex-trading-ai-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.