Crypto Research Skill for Claude

Cryptocurrency Research Skill

This skill provides comprehensive cryptocurrency research by orchestrating multiple specialized AI agents that analyze different aspects of the crypto market in parallel.

When to Use

Invoke this skill when the user:

• Mentions cryptocurrency analysis or research
• Names specific cryptocurrencies (BTC, ETH, SOL, etc.)
• Asks about crypto market conditions
• Wants investment analysis or opportunities
• Needs technical or fundamental analysis of crypto assets
• Requests macro correlation analysis
• Asks about crypto news or sentiment

Capabilities

Multi-Agent Research System

Coordinates 4-12 specialized agents running in parallel:

• Market Agent: Overall market conditions and trends
• Coin Analyzer: Deep dive on specific cryptocurrencies
• Macro Correlation Scanner: Relationships with traditional markets
• Investment Plays Agent: Opportunity identification
• News Scanner: Recent developments and sentiment
• Price Check: Real-time price and volume data
• Movers Agent: Biggest gainers and losers

Research Modes

• Comprehensive Mode: All agents (12 total) across 3 model types (haiku, sonnet, opus)
• Lightweight Mode: Haiku agents only (4 agents) for quick analysis
• Output-Only Mode: Silent execution with file output only

Output Organization

Research results are saved in timestamped directories:

outputs/
└── YYYY-MM-DD_HH-MM-SS/
    ├── crypto_market/
    ├── crypto_analysis/
    ├── crypto_macro/
    ├── crypto_plays/
    └── crypto_news/

How It Works

1. Mode Selection

Based on user request or context:

• Quick question: Use lightweight mode (4 haiku agents)
• Comprehensive research: Use full mode (12 agents)
• Background analysis: Use output-only mode

2. Agent Orchestration

• Run date command to get timestamp
• Create output directory structure using scripts/setup-output-dir.sh
• Launch agents in parallel using Task tool
• Each agent writes results to designated file
• Present summary with file locations

3. Agent Coordination

Agents are defined in agent-prompts/ directory:

• coin-analyzer.md - Receives ticker symbol parameter
• market-agent.md - General market analysis
• macro-correlation-scanner.md - Correlation analysis
• investment-plays.md - Investment opportunities
• news-scanner.md - News aggregation
• price-check.md - Current pricing data
• movers.md - Top movers analysis

Each agent prompt includes:

• Purpose and specialization
• Data gathering instructions (5+ tools)
• Output format requirements
• Timestamp and timezone handling

Workflows

Quick Research (Default)

See workflows/lightweight.md for implementation details.

When: User asks quick question about crypto

Agents: 4 haiku agents

Duration: ~30-60 seconds

Comprehensive Research

See workflows/comprehensive.md for implementation details.

When: User needs deep analysis or multiple perspectives

Agents: 12 agents (haiku, sonnet, opus variations)

Duration: ~2-5 minutes

Silent Research

See workflows/output-only.md for implementation details.

When: Background research or automated workflows

Agents: Configurable

Output: Files only, no interactive output

Usage Examples

Example 1: Specific Coin Analysis

User: "What's happening with Bitcoin?"
Action: Launch lightweight mode with BTC as ticker
Agents: 4 haiku agents analyzing Bitcoin specifically
Output: Quick analysis in ~30 seconds

Example 2: Market Overview

User: "How are crypto markets doing today?"
Action: Launch market-focused agents
Agents: Market agent + movers + macro correlation
Output: Market overview with key movers

Example 3: Investment Research

User: "I'm looking for good crypto investment opportunities"
Action: Launch comprehensive mode
Agents: All 12 agents for multi-perspective analysis
Output: Comprehensive report with opportunities

Agent Parameters

TICKER Variable

Coin analyzer agents accept a ticker symbol:

• Default: "BTC" if not specified
• Examples: BTC, ETH, SOL, ADA, DOT, AVAX, etc.
• Used by: coin-analyzer agents (haiku, sonnet, opus)

Model Selection

• Haiku: Fast, cost-effective, good for quick analysis
• Sonnet: Balanced, default for most research
• Opus: Deep analysis, best quality, slower and more expensive

Error Handling

If agents fail or timeout:

• Check agent output files for partial results
• Retry failed agents individually
• Report which agents completed successfully
• Provide path to output directory for user inspection

Best Practices

• Start with Lightweight: Use haiku mode for initial questions
• Upgrade to Comprehensive: When deeper analysis needed
• Specify Tickers: Be explicit about which cryptocurrencies to analyze
• Check Timestamps: Results include generation time for data freshness
• Review All Outputs: Different agents may catch different insights

Progressive Disclosure

For detailed information, see:

• reference/agent-design.md - How agents are structured
• reference/usage-guide.md - Detailed usage instructions
• workflows/*.md - Specific workflow implementations

Version History

• v1.0.0 (2025-01): Initial skill creation from command refactoring