activer007/crypto-research
skills_categorized/defi/crypto-research/SKILL.md
Comprehensive cryptocurrency market research and analysis using specialized AI agents. Analyzes market data, price trends, news sentiment, technical indicators, macro correlations, and investment opportunities. Use when researching cryptocurrencies, analyzing crypto markets, evaluating digital assets, or investigating blockchain projects like Bitcoin, Ethereum, Solana, etc.
$ install --global
skillsauthnpx skillsauth add activer007/ordinary-claude-skills crypto-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- crypto-research
- description:
- Comprehensive cryptocurrency market research and analysis using specialized AI agents. Analyzes market data, price trends, news sentiment, technical indicators, macro correlations, and investment opportunities. Use when researching cryptocurrencies, analyzing crypto markets, evaluating digital assets, or investigating blockchain projects like Bitcoin, Ethereum, Solana, etc.
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
datecommand 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 parametermarket-agent.md- General market analysismacro-correlation-scanner.md- Correlation analysisinvestment-plays.md- Investment opportunitiesnews-scanner.md- News aggregationprice-check.md- Current pricing datamovers.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 structuredreference/usage-guide.md- Detailed usage instructionsworkflows/*.md- Specific workflow implementations
Version History
- v1.0.0 (2025-01): Initial skill creation from command refactoring
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