#!/opt/anaconda3/envs/mcpskills/bin/python3
"""
Deep Research Phase using Claude Agent SDK with MCP Tools
Reads research_report.md and performs comprehensive deep analysis
using Claude Sonnet 4.5 with extended thinking and access to
6 MCP servers providing financial research tools.
Usage:
./skills/research_deep.py SYMBOL [--work-dir DIR] [--no-tools]
Output:
- 08_deep_research/deep_research_output.md
- 08_deep_research/deep_research_thinking.md
- 08_deep_research/tool_usage.txt (if tools used)
"""
import os
import sys
import argparse
import asyncio
import json
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
import dotenv
# Try Claude Agent SDK imports, fall back to basic Anthropic if not available
try:
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
AssistantMessage,
TextBlock
)
AGENT_SDK_AVAILABLE = True
except ImportError:
AGENT_SDK_AVAILABLE = False
print("⚠ Warning: claude-agent-sdk not installed. MCP tools will not be available.")
print(" Install with: pip install claude-agent-sdk")
# Basic Anthropic SDK for fallback
from anthropic import Anthropic
def load_research_report(work_dir):
"""Load the initial research report as context."""
report_path = os.path.join(work_dir, 'research_report.md')
if not os.path.exists(report_path):
raise FileNotFoundError(f"Research report not found: {report_path}")
with open(report_path, 'r') as f:
content = f.read()
# Extract company name from report (first line after "Company:")
company_name = None
for line in content.split('\n'):
if line.startswith('**Company:**'):
company_name = line.replace('**Company:**', '').strip()
break
return content, company_name
def load_mcp_server_configs() -> Dict[str, Any]:
"""
Load MCP server configurations from claude_desktop_config.json.
Returns:
dict: MCP server configurations for ClaudeAgentOptions
"""
config_path = os.path.expanduser(
"~/Library/Application Support/Claude/claude_desktop_config.json"
)
if not os.path.exists(config_path):
raise FileNotFoundError(f"Claude config not found: {config_path}")
with open(config_path, 'r') as f:
claude_config = json.load(f)
mcp_servers = {}
# Target servers for deep research
# Testing with single server to avoid Claude Agent SDK bug with multiple servers
# Bug: https://github.com/anthropics/claude-code/issues/10668
server_names = [
'stock-symbol-server', # Our custom financial tools (6 tools)
]
for server_name in server_names:
if server_name in claude_config.get('mcpServers', {}):
server_config = claude_config['mcpServers'][server_name]
# Convert to ClaudeAgentOptions format
mcp_servers[server_name] = {
'type': 'stdio',
'command': server_config['command'],
'args': server_config.get('args', []),
'env': server_config.get('env', {})
}
return mcp_servers
async def run_deep_research_with_tools(
symbol: str,
company_name: str,
report_content: str
) -> Tuple[str, str, str]:
"""
Call Claude Agent SDK with MCP tools and research report context.
Hybrid mode: Provides research_report.md as context while giving
Claude access to all MCP tools for verification and gap-filling.
Note: API key is handled via ~/.claude/ configuration, not environment variables.
Returns:
tuple: (analysis_output, thinking_process, tool_usage_summary)
"""
# Load MCP server configurations
print("\n" + "="*60)
print("MCP SERVER CONFIGURATION")
print("="*60)
try:
mcp_servers = load_mcp_server_configs()
print(f"✓ Loaded {len(mcp_servers)} MCP server configurations:")
for server_name, config in mcp_servers.items():
print(f"\n 📡 {server_name}:")
print(f" Type: {config.get('type')}")
print(f" Command: {config.get('command')}")
print(f" Args: {config.get('args', [])}")
if config.get('env'):
print(f" Env vars: {list(config['env'].keys())}")
except FileNotFoundError as e:
print(f"⚠️ Claude Desktop config not found: {e}")
print(f" Expected at: ~/Library/Application Support/Claude/claude_desktop_config.json")
print(f" Continuing in basic mode (no MCP tools)")
mcp_servers = {}
except Exception as e:
print(f"⚠️ Could not load MCP servers: {e}")
print(f" Continuing in basic mode (no MCP tools)")
import traceback
traceback.print_exc()
mcp_servers = {}
if not mcp_servers:
print(f"\n⚠️ No MCP servers configured - tools will NOT be available")
print(f" To enable MCP tools, configure servers in Claude Desktop config")
print("="*60)
# Create Claude Agent options
# Note: API key is handled via ~/.claude/ configuration
# Model defaults to claude-sonnet-4-5
options = ClaudeAgentOptions(
mcp_servers=mcp_servers,
cwd=os.getcwd(),
# Auto-approve tool usage for autonomous research
allowed_tools=["*"], # Allow all MCP tools
)
print(f"\n⚙️ Agent Options:")
print(f" MCP Servers: {list(mcp_servers.keys())}")
print(f" Allowed Tools: {options.allowed_tools}")
print(f" CWD: {options.cwd}")
company_display = company_name if company_name else symbol
# Enhanced prompt for hybrid mode (will be updated with tools list)
prompt_template = f"""I have collected comprehensive information on {company_display} (symbol {symbol}) in the research_report.md file provided below.
You have access to powerful financial research tools through MCP servers. Use these tools to:
- Fill gaps in the provided research
- Verify critical facts and figures
- Find recent developments not covered in the report
- Gather additional market data and competitive intelligence
Write a comprehensive report on {symbol} in the straightforward factual style of a Wall Street equity research analyst, in 9 sections:
1. Short summary overall assessment:
One-sentence summary of section 9 with evaluation of risk/reward profile
2. Extended Profile:
• History with origin story and key historical milestones
• Core business and competitors
• Recent major news
3. Business Model:
• Describe their core businesses, products and services.
• Outline their key revenue streams, customer segments, and monetization strategies.
• Analyze key characteristics of markets it operates in:
- customer acquisition costs
- retention metrics
- sales cycles
- seasonal or cyclical business patterns
- margins, market size, growth trajectory and factors affecting them
• Explain sources of competitive advantage such as network effects, switching costs, brands, intellectual property, regulatory moats, and other barriers to entry.
4. Competitive Landscape:
• Identify their main competitors, including direct, adjacent, and emerging competitors.
• Compare key metrics such as market share, product differentiation, pricing power, and growth trajectories.
5. Supply Chain Positioning:
• Describe their role in the upstream (supplier-side) and downstream (customer/distribution) parts of the supply chain.
• Identify key suppliers, partners, distributors, and any major dependencies or concentrations.
6. Financial and Operating Leverage:
• Analyze the company's use of financial leverage (debt levels, interest obligations, credit ratings).
• Analyze operating leverage (fixed vs. variable cost structure, scalability, margin sensitivity to revenue changes).
• Analyze cash flow generation and working capital dynamics.
• Analyze capital allocation strategy (dividends, buybacks, reinvestment).
7. Valuation:
• Identify appropriate valuation methodologies, including income-based (e.g., DCF), asset-based (e.g. book value and sum of parts), market-based (e.g. peer multiples and comparisons), and LBO analysis
• Highlight important valuation inputs and metrics (growth rates, margins, discount rates, terminal value assumptions).
• Summarize current ratings and analyst opinions, including recent changes.
• Note the stock's volatility, liquidity, if it is widely covered and owned, if it is a hedge fund story stock or meme stock, what macro factors it is sensitive to
8. Recent developments, News Search and Risk Factors:
For this section, use your MCP tools to conduct deep research:
• Search for: {company_display} + "analyst report" OR "research note" downgrade OR upgrade
• Use perplexity to search: "What are the most significant investigative reports and executive profiles about {company_display} published in 2024-2025?"
• Look for: revenue trends, management changes, product launches, M&A, restructurings
• Investigate: short-seller reports, regulatory investigations, lawsuits
• Check for: product failures, supply chain disruptions
• Find: major wins, partnerships, large customer acquisitions
• Research: insider trading patterns and institutional ownership changes
• Consider: potential acquirers or acquisition targets
• Note any other key themes or trends that you think are important
9. Conclusion:
• Summarize the company's strategic position and the stock's investment risk/reward profile
• Strengths, weaknesses, opportunities, threats (SWOT)
• Bear case and bull case
• The level of risk
• Any critical "watch points" for further due diligence and ongoing monitoring
Use a straightforward, factual tone throughout the analysis. Focus on data and observable facts rather than speculation.
{{TOOLS_SECTION}}
---
Here is the research report collected so far:
{report_content}
"""
print(f"\nInitializing Claude Agent SDK client...")
print(f"Model: claude-sonnet-4-5-20250929")
print(f"MCP servers: {len(mcp_servers)}")
try:
async with ClaudeSDKClient(options=options) as client:
print(f"✓ Claude Agent SDK client initialized")
# Enumerate available MCP tools
print(f"\n🔧 Enumerating available MCP tools...")
available_tools_list = []
tools_description = ""
try:
# Get available tools from MCP servers
available_tools = []
if hasattr(client, 'tools'):
available_tools = client.tools
elif hasattr(client, 'get_tools'):
available_tools = await client.get_tools()
elif hasattr(client, '_tools'):
available_tools = client._tools
if available_tools:
print(f"✓ Found {len(available_tools)} available tools:")
for tool in available_tools:
if hasattr(tool, 'name'):
tool_name = tool.name
tool_desc = getattr(tool, 'description', 'No description')
print(f" • {tool_name}: {tool_desc[:80]}")
available_tools_list.append(f"{tool_name}: {tool_desc}")
else:
print(f" • {tool}")
available_tools_list.append(str(tool))
# Create tools description for prompt
tools_description = "\n\nAVAILABLE MCP TOOLS:\n" + "\n".join(
f"- {tool}" for tool in available_tools_list
) + "\n\nUse these tools extensively to enhance your research.\n"
else:
print(f"⚠️ No tools found (tools attribute not available)")
print(f" Client attributes: {[attr for attr in dir(client) if not attr.startswith('_')]}")
tools_description = "\n\n⚠️ No MCP tools available for this analysis.\n"
except Exception as tool_enum_error:
print(f"⚠️ Could not enumerate tools: {tool_enum_error}")
print(f" MCP servers may still work, but tool list unavailable")
import traceback
traceback.print_exc()
tools_description = "\n\n⚠️ Could not enumerate MCP tools, but they may still be available.\n"
print(f"\n🚀 Starting agentic research loop...")
print(f"This may take several minutes as Claude uses tools...")
# Insert tools description into prompt
final_prompt = prompt_template.replace("{TOOLS_SECTION}", tools_description)
# Debug: Show prompt length
print(f"\n📝 Prompt length: {len(final_prompt)} characters")
if available_tools_list:
print(f" Including {len(available_tools_list)} MCP tools in prompt")
# Send initial query
await client.query(final_prompt)
# Collect response and thinking
analysis_blocks = []
thinking_blocks = []
tool_usage = []
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
print(f"\n📨 Received AssistantMessage with {len(message.content)} blocks")
for i, block in enumerate(message.content):
# Debug: print block type and attributes
block_type = type(block).__name__
block_type_attr = getattr(block, 'type', None)
print(f" 📦 Block {i+1}: {block_type} (type={block_type_attr})")
# Handle TextBlock
if isinstance(block, TextBlock):
analysis_blocks.append(block.text)
print(f" ✓ Text captured ({len(block.text)} chars)")
# Handle Thinking blocks (check both ways)
elif (hasattr(block, 'type') and block.type == 'thinking') or block_type == 'ThinkingBlock':
if hasattr(block, 'thinking'):
thinking_blocks.append(block.thinking)
print(f" 💭 Thinking captured ({len(block.thinking)} chars)")
else:
print(f" ⚠️ ThinkingBlock has no 'thinking' attribute")
# Handle Tool Use blocks (check multiple ways)
elif (hasattr(block, 'type') and block.type == 'tool_use') or \
block_type == 'ToolUseBlock' or \
hasattr(block, 'tool_use'):
# Extract tool info
if hasattr(block, 'name'):
# Direct attributes (ToolUseBlock)
tool_name = block.name
tool_input = getattr(block, 'input', {})
tool_id = getattr(block, 'id', 'unknown')
elif hasattr(block, 'tool_use'):
# Nested tool_use object
tool_name = block.tool_use.name
tool_input = block.tool_use.input
tool_id = getattr(block.tool_use, 'id', 'unknown')
else:
tool_name = 'unknown'
tool_input = {}
tool_id = 'unknown'
tool_info = f"{tool_name}(id={tool_id[:8]}, input={str(tool_input)[:60]}...)"
tool_usage.append({
'type': 'call',
'name': tool_name,
'id': tool_id,
'input': tool_input,
'timestamp': datetime.now().isoformat()
})
print(f" 🔧 Tool call: {tool_name}")
print(f" ID: {tool_id}")
print(f" Input: {str(tool_input)[:100]}")
# Handle Tool Result blocks
elif (hasattr(block, 'type') and block.type == 'tool_result') or \
block_type == 'ToolResultBlock':
tool_id = getattr(block, 'tool_use_id', 'unknown')
content = getattr(block, 'content', '')
is_error = getattr(block, 'is_error', False)
# Check for errors in result
if is_error or (hasattr(block, 'status') and block.status == 'error'):
error_msg = str(content)[:200]
print(f" ❌ Tool error for ID {tool_id[:8]}: {error_msg}")
tool_usage.append({
'type': 'error',
'id': tool_id,
'error': error_msg,
'timestamp': datetime.now().isoformat()
})
else:
result_preview = str(content)[:100]
print(f" 🎯 Tool result for ID {tool_id[:8]}: {result_preview}...")
tool_usage.append({
'type': 'result',
'id': tool_id,
'preview': result_preview,
'timestamp': datetime.now().isoformat()
})
else:
# Unknown block type - detailed debugging
print(f" ⚠️ Unknown block type: {block_type}")
print(f" Attributes: {[attr for attr in dir(block) if not attr.startswith('_')]}")
analysis = "\n\n".join(analysis_blocks)
thinking = "\n\n---\n\n".join(thinking_blocks)
# Generate detailed tool usage summary
if tool_usage:
tool_calls = [t for t in tool_usage if t.get('type') == 'call']
tool_errors = [t for t in tool_usage if t.get('type') == 'error']
tool_results = [t for t in tool_usage if t.get('type') == 'result']
summary_lines = [
f"MCP TOOL USAGE SUMMARY",
f"=" * 60,
f"Total tool calls: {len(tool_calls)}",
f"Successful results: {len(tool_results)}",
f"Errors: {len(tool_errors)}",
f"",
f"TOOL CALLS:",
f"-" * 60
]
for call in tool_calls:
summary_lines.append(f"\n• {call['name']} (ID: {call['id'][:8]})")
summary_lines.append(f" Time: {call['timestamp']}")
summary_lines.append(f" Input: {str(call['input'])[:100]}...")
if tool_errors:
summary_lines.append(f"\n\nERRORS:")
summary_lines.append(f"-" * 60)
for error in tool_errors:
summary_lines.append(f"\n❌ Tool ID {error['id'][:8]}")
summary_lines.append(f" Time: {error['timestamp']}")
summary_lines.append(f" Error: {error['error']}")
tool_summary = "\n".join(summary_lines)
else:
tool_summary = "No MCP tools were used in this analysis."
print(f"\n✓ Analysis complete:")
print(f" - Analysis output: {len(analysis)} characters")
print(f" - Thinking blocks: {len(thinking_blocks)}")
print(f" - Tool calls: {len([t for t in tool_usage if t.get('type') == 'call'])}")
return analysis, thinking, tool_summary
except Exception as e:
print(f"\n❌ Error in Claude Agent SDK: {e}")
print("\nFalling back to basic mode without tools...")
import traceback
traceback.print_exc()
# Fall back to basic mode
return run_deep_research_basic(symbol, company_name, report_content)
def run_deep_research_basic(symbol, company_name, report_content):
"""
Fallback: Basic Anthropic SDK without tools.
This is the existing implementation for graceful degradation
when MCP servers fail to connect or Agent SDK is not available.
Returns:
tuple: (analysis_output, thinking_process, None)
"""
dotenv.load_dotenv()
api_key = os.environ.get('ANTHROPIC_API_KEY')
if not api_key:
raise ValueError("ANTHROPIC_API_KEY environment variable not set")
client = Anthropic(api_key=api_key)
# Use company_name if available, otherwise use symbol
company_display = company_name if company_name else symbol
prompt = f"""I have collected information on {company_display} (symbol {symbol}) from various tools in the research_report.md file.
Using everything found so far and everything you can find by using tools and doing deep research, write a report on {symbol} in the straightforward factual style of a Wall Street equity research analyst, in 9 sections:
Include links to sources at the end of each paragraph.
1. Introduction:
One-sentence summary of section 9 with evaluation of risk/reward profile
2. Company Profile:
• History with origin story and key historical milestones
• Core business and competitors
• Recent major news
3. Business Model:
• Describe their core businesses, products and services.
• Outline their key revenue streams, customer segments, and monetization strategies.
• Analyze key characteristics of markets it operates in:
- customer acquisition costs
- retention metrics
- sales cycles
- seasonal or cyclical business patterns
- margins, market size, growth trajectory and factors affecting them
• Explain sources of competitive advantage such as network effects, switching costs, brands, intellectual property, regulatory moats, and other barriers to entry.
4. Competitive Landscape:
• Identify their main competitors, including direct, adjacent, and emerging competitors.
• Compare key metrics such as market share, product differentiation, pricing power, and growth trajectories.
5. Supply Chain Positioning:
• Describe their role in the upstream (supplier-side) and downstream (customer/distribution) parts of the supply chain.
• Identify key suppliers, partners, distributors, and any major dependencies or concentrations.
6. Financial and Operating Leverage:
• Analyze the company's use of financial leverage (debt levels, interest obligations, credit ratings).
• Analyze operating leverage (fixed vs. variable cost structure, scalability, margin sensitivity to revenue changes).
• Analyze cash flow generation and working capital dynamics.
• Analyze capital allocation strategy (dividends, buybacks, reinvestment).
7. Valuation:
• Identify appropriate valuation methodologies, including income-based (e.g., DCF), asset-based (e.g. book value and sum of parts), market-based (e.g. peer multiples and comparisons), and LBO analysis
• Highlight important valuation inputs and metrics (growth rates, margins, discount rates, terminal value assumptions).
• Summarize current ratings and analyst opinions, including recent changes.
• Note the stock's volatility, liquidity, if it is widely covered and owned, if it is a hedge fund story stock or meme stock, what macro factors it is sensitive to
8. Recent developments, News Search and Risk Factors:
• Conduct a deep news search for significant positive and negative news items over the last 12 months, including:
- company + "analyst report" OR "research note" downgrade OR upgrade
- use perplexity to search for company + "profile" or "executive profile" and ask: "What are the most significant investigative reports and executive profiles about {company_display} published in 2023-2024?"
- Revenue and earnings trends
- Management changes
- New product launches
- Restructurings, mergers, acquisitions, divestitures, strategic partnerships
- Short-seller reports or allegations.
- Regulatory investigations or lawsuits.
- Product failures, operational issues, or supply chain disruptions.
- Major wins (e.g., partnerships, large customer wins, successful product launches).
- Insider trading activity and institutional ownership changes
• Summarize key themes from recent media coverage, analyst reports, and public filings.
• Note controversies, reputational risks, and governance concerns if any.
• Discuss what companies might be potential acquisition targets or acquirers of the company, based on overlapping or complementary customer bases, product offerings, and technical capabilities.
• Note any other key themes or trends that you think are important.
9. Conclusion:
• Summarize the company's strategic position and the stock's investment risk/reward profile
• Strengths, weaknesses, opportunities, threats (SWOT)
• Bear case and bull case
• The level of risk
• Any critical "watch points" for further due diligence and ongoing monitoring.
Use a straightforward, factual tone throughout the analysis. Focus on data and observable facts rather than speculation.
"""
print(f"\nCalling Claude API...")
print(f"Model: claude-sonnet-4-5-20250929")
print(f"Extended thinking: Enabled (10K token budget)")
try:
message = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=16000,
thinking={
"type": "enabled",
"budget_tokens": 10000
},
messages=[
{
"role": "user",
"content": f"{prompt}\n\n---\n\nHere is the research report:\n\n{report_content}"
}
]
)
# Extract thinking and text content
thinking_blocks = []
text_blocks = []
print(f"\n📦 Processing {len(message.content)} content blocks...")
for block in message.content:
block_type = block.type
print(f" Block type: {block_type}")
if block.type == "thinking":
thinking_blocks.append(block.thinking)
print(f" 💭 Captured thinking block ({len(block.thinking)} chars)")
elif block.type == "text":
text_blocks.append(block.text)
print(f" 📝 Captured text block ({len(block.text)} chars)")
else:
print(f" ⚠️ Unknown block type: {block_type}")
print(f"\n✓ Extracted {len(text_blocks)} text blocks and {len(thinking_blocks)} thinking blocks")
analysis = "\n\n".join(text_blocks)
thinking = "\n\n---\n\n".join(thinking_blocks)
return analysis, thinking, None
except Exception as e:
print(f"\n❌ Error calling Claude API: {e}")
raise
def save_outputs(work_dir, analysis, thinking, tool_summary=None):
"""Save deep research outputs including tool usage summary."""
output_dir = os.path.join(work_dir, '08_deep_research')
os.makedirs(output_dir, exist_ok=True)
# Save analysis
analysis_path = os.path.join(output_dir, 'deep_research_output.md')
with open(analysis_path, 'w') as f:
f.write(analysis)
print(f"✓ Saved: {analysis_path}")
# Save thinking process
thinking_path = os.path.join(output_dir, 'deep_research_thinking.md')
with open(thinking_path, 'w') as f:
f.write(f"# Extended Thinking Process\n\n")
if thinking and thinking.strip():
f.write(thinking)
else:
f.write("No extended thinking was captured.\n\n")
f.write("This could mean:\n")
f.write("- The model didn't generate thinking output\n")
f.write("- Extended thinking was not enabled in the API call\n")
f.write("- Thinking blocks were filtered/redacted by safety systems\n")
print(f"✓ Saved: {thinking_path}")
# Save tool usage summary
tool_path = os.path.join(output_dir, 'tool_usage.txt')
with open(tool_path, 'w') as f:
if tool_summary:
f.write(tool_summary)
else:
f.write("Tools used (0):\nNo MCP tools were used (basic mode or tools not available)")
print(f"✓ Saved: {tool_path}")
return True
def main():
parser = argparse.ArgumentParser(
description='Deep research phase using Claude Agent SDK with MCP tools'
)
parser.add_argument('symbol', help='Stock ticker symbol')
parser.add_argument(
'--work-dir',
default=None,
help='Work directory path (default: work/SYMBOL_YYYYMMDD)'
)
parser.add_argument('--no-tools', action='store_true',
help='Disable MCP tools (basic mode only)')
args = parser.parse_args()
# Normalize symbol
symbol = args.symbol.upper()
# Generate work directory if not specified
if not args.work_dir:
date_str = datetime.now().strftime('%Y%m%d')
work_dir = os.path.join('work', f'{symbol}_{date_str}')
else:
work_dir = args.work_dir
# Create work directory if it doesn't exist
os.makedirs(work_dir, exist_ok=True)
print("="*60)
print("Deep Research Phase - Claude Agent SDK with MCP Tools")
print("="*60)
print(f"Symbol: {symbol}")
print(f"Work Directory: {work_dir}")
print(f"Tools Enabled: {not args.no_tools}")
if not AGENT_SDK_AVAILABLE:
print(f"⚠ Agent SDK not available - forced basic mode")
print("="*60)
try:
# Load research report
print(f"\nLoading research report...")
report_content, company_name = load_research_report(work_dir)
print(f"✓ Loaded research report ({len(report_content)} characters)")
if company_name:
print(f"✓ Company: {company_name}")
# Run deep research
print(f"\nAnalyzing {symbol} with Claude Sonnet 4.5...")
if args.no_tools or not AGENT_SDK_AVAILABLE:
if args.no_tools:
print("Basic mode (--no-tools flag)")
else:
print("Basic mode (Agent SDK not available)")
analysis, thinking, tool_summary = run_deep_research_basic(
symbol, company_name, report_content
)
else:
# Run async function with MCP tools
print("Advanced mode (MCP tools enabled)")
analysis, thinking, tool_summary = asyncio.run(
run_deep_research_with_tools(symbol, company_name, report_content)
)
print(f"✓ Analysis complete ({len(analysis)} characters)")
if thinking:
print(f"✓ Thinking process captured ({len(thinking)} characters)")
if tool_summary:
print(f"✓ Tool usage recorded")
# Save outputs
print(f"\nSaving outputs...")
save_outputs(work_dir, analysis, thinking, tool_summary)
print("\n" + "="*60)
print("SUCCESS: Deep research completed!")
print("="*60)
return 0
except FileNotFoundError as e:
print(f"\n❌ Error: {e}")
print("\nMake sure research_report.md has been generated first.")
print("Run with --phases report before running deep research.")
return 1
except ValueError as e:
print(f"\n❌ Error: {e}")
print("\nMake sure ANTHROPIC_API_KEY is set in your .env file.")
return 1
except Exception as e:
print(f"\n❌ Error in deep research: {e}")
import traceback
traceback.print_exc()
return 1
if __name__ == '__main__':
sys.exit(main())
