analyze
Run a comprehensive multi-agent crypto analysis with phased execution. Usage: /analyze BTC or /analyze ETH SOL
Review and validate pending predictions against current market data. Usage: /validate-predictions
Claude Code
$ npx -y skills add hugoguerrap/crypto-claude-desk --skill validate-predictions --agent claude-code
Codex
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a codex -y
Cursor
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a cursor -y
Gemini CLI
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a gemini-cli -y
GitHub Copilot
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a github-copilot -y
OpenCode
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a opencode -y
Windsurf
$ npx skills add hugoguerrap/crypto-claude-desk --skill validate-predictions -a windsurf -y
name: validate-predictions description: Review and validate pending predictions against current market data. Usage: /validate-predictions user-invocable: true
Review all pending predictions and check them against current market data.
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. First, use get_crypto_prices() from crypto-data MCP to get current prices for major coins (bitcoin, ethereum, solana, etc.). Then call find_expired_predictions(current_prices='{\"BTC/USDT\": ..., \"ETH/USDT\": ...}') from crypto-learning-db to discover predictions whose timeframe has passed. For each expired prediction, reason about how close it was and validate with an NL evaluation using validate_prediction(). Do NOT use the Edit tool."
Delegate using the Task tool with `subagent_type: general-purpose` and `model: opus`:
"You are the learning-agent. Call query_predictions(status='pending') from crypto-learning-db for predictions still within their timeframe. For each prediction: 1. Use get_exchange_prices(symbol=...) from crypto-exchange MCP to check current price 2. Compare current price against the prediction's target_value 3. Report current progress toward or away from target Do NOT use the Edit tool."
Show a summary table:
## Prediction Validation Report ### Resolved This Check | ID | Agent | Prediction | Target | Actual | Result | |----|-------|-----------|--------|--------|--------| ### Still Pending | ID | Agent | Prediction | Target | Current | Progress | Expires | |----|-------|-----------|--------|---------|----------|---------| ### Overall Accuracy - Total predictions: X - Correct: X (X%) - Incorrect: X (X%) - Pending: X ### Track Record by Setup Type | Setup Type | Total | Correct | Accuracy | Trend | |-----------|-------|---------|----------|-------|
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.
Run a comprehensive multi-agent crypto analysis with phased execution. Usage: /analyze BTC or /analyze ETH SOL
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