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/close-trade

Close an open trade and run post-mortem analysis. Usage: /close-trade trade_001 or /close-trade trade_001 at 98500

From plugin
crypto-trading-desk
338 skills7 agents1 hook
Install
$ npx -y skills add hugoguerrap/crypto-claude-desk --skill close-trade --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/close-trade

Context 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

SKILL.md

close-trade.SKILL.md
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 & Post-Mortem

Close trade $ARGUMENTS and run a post-mortem analysis.

Workflow

Step 1: Close the Trade

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."

Step 2: Post-Mortem Analysis

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."

Step 3: Validate Predictions & Update Patterns

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."

Step 4: Present Results

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

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Ships withcrypto-trading-desk

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.

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