analyze
Run a comprehensive multi-agent crypto analysis with phased execution. Usage: /analyze BTC or /analyze ETH SOL
Close an open trade and run post-mortem analysis. Usage: /close-trade trade_001 or /close-trade trade_001 at 98500
$ npx -y skills add hugoguerrap/crypto-claude-desk --skill close-trade --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/close-tradeContext preview
The summary Claude sees to decide when to auto-load this skill.
Close an open trade and run post-mortem analysis. Usage: /close-trade trade_001 or /close-trade trade_001 at 98500
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 user-invocable: true
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."
Show: 1. Trade closure summary (entry, exit, PnL) 2. Post-mortem analysis 3. Prediction accuracy (how many correct vs incorrect, with evaluations) 4. Pattern identified (win rate, recommendation) 5. Lessons learned
I used to spend weeks building multi-agent systems with LangGraph, CrewAI, and AutoGen. Hundreds of lines of Python orchestration code, custom state machines, fragile message passing between agents.
Repo: hugoguerrap/crypto-claude-desk
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
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View current portfolio status, open trades, and performance stats. Usage: /portfolio
First-time setup for Crypto Trading Desk. Detects your environment, installs dependencies, and verifies everything works. Run this once after installing the…