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Close an open trade and run post-mortem analysis. Usage: /close-trade trade_001 or /close-trade trade_001 at 98500
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
Run a comprehensive multi-agent crypto analysis with phased execution. Usage: /analyze BTC or /analyze ETH SOL
Extend the system by creating new MCP servers, agents, or skills. Usage: /create a DeFi protocol tracker or /create an agent for macro analysis
Autonomous monitoring loop. Checks open trades against SL/TP levels, closes trades that hit targets, evaluates expired predictions, and generates periodic summaries. Run via cron for full autonomy. Usage: /monitor
View current portfolio status, open trades, and performance stats. Usage: /portfolio
Quick single-agent market check. Usage: /quick BTC or /quick ETH
First-time setup for Crypto Trading Desk. Detects your environment, installs dependencies, and verifies everything works. Run this once after installing the plugin.
| name | close-trade |
| description | Close an open trade and run post-mortem analysis. Usage: /close-trade trade_001 or /close-trade trade_001 at 98500 |
Close trade $ARGUMENTS and run a post-mortem analysis.
Delegate using the Task tool with subagent_type: general-purpose and model: opus:
"You are the portfolio-manager agent. Read agents/portfolio-manager.md for your decision framework. Close trade $ARGUMENTS. If a price is specified after 'at', use that as exit price. Otherwise, get the current market price using get_exchange_prices() from crypto-exchange MCP. Call close_trade(trade_id='...', exit_price=..., close_reason='...') from crypto-learning-db MCP. PnL, portfolio balance, and stats are updated automatically. Do NOT use the Edit tool."
After the trade is closed, delegate using the Task tool with subagent_type: general-purpose and model: opus:
"You are the learning-agent. Read agents/learning-agent.md for your analysis framework. Run a post-mortem analysis on the recently closed trade $ARGUMENTS. Call query_trades(status='closed', limit=1) from crypto-learning-db to get the trade data. Read any related reports from data/reports/. Analyze what worked, what didn't, and provide specific recommendations for improvement. Do NOT use the Edit tool."
After the post-mortem, delegate using the Task tool with subagent_type: general-purpose and model: opus:
"You are the learning-agent. Validate all predictions for trade $ARGUMENTS. Call query_predictions(trade_id='...') from crypto-learning-db to find all predictions tied to this trade. Compare each prediction against the actual outcome. Call validate_prediction() for each one with a detailed NL evaluation of how close the prediction was and what we can learn. Then call upsert_pattern() to update the pattern library with the setup from this trade. Do NOT use the Edit tool."
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