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/validate-predictions

Review and validate pending predictions against current market data. Usage: /validate-predictions

From plugin
crypto-trading-desk
338 skills7 agents1 hook
Install
$ npx -y skills add hugoguerrap/crypto-claude-desk --skill validate-predictions --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/validate-predictions

Context preview

The summary Claude sees to decide when to auto-load this skill.

Review and validate pending predictions against current market data. Usage: /validate-predictions

SKILL.md

validate-predictions.SKILL.md
name: validate-predictions
description: Review and validate pending predictions against current market data. Usage: /validate-predictions
user-invocable: true

Validate Predictions

Review all pending predictions and check them against current market data.

Workflow

Step 0: Auto-Find Expired Predictions

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."

Step 1: Check Remaining Pending Predictions

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."

Step 2: Present Results

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 |
|-----------|-------|---------|----------|-------|
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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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