refactor: update CLI options to use 'mode' instead of 'agent', enhance documentation for execution modes and examples

Author: OrionLi545

README.md

@@ -32,16 +32,16 @@ uvx agentic-data-scientist "your query here"
 #### CLI
 
 ```bash
-# Simple query
+# Simple query (uses orchestrated mode by default)
 agentic-data-scientist "Explain quantum computing"
 
 # With file upload
 agentic-data-scientist "Analyze this data" --files data.csv
 
-# Use Claude Code agent
-agentic-data-scientist "Write a Python script" --agent claude_code
+# Simple mode (direct Claude Code without orchestration)
+agentic-data-scientist "Write a Python script" --mode simple
 
-# Stream responses
+# Stream responses in real-time
 agentic-data-scientist "Complex analysis" --stream
 ```
 

docs/api_reference.md

@@ -271,29 +271,31 @@ agentic-data-scientist [OPTIONS] QUERY
 
 ### Options
 
-- **--agent, -a**: Agent type (`adk` or `claude_code`), default: `adk`
+- **--mode**: Execution mode, default: `orchestrated`
+  - `orchestrated`: Full multi-agent system with planning and verification
+  - `simple`: Direct Claude Code execution
 - **--files, -f**: Files to upload (can be specified multiple times)
 - **--stream**: Enable streaming output
-- **--model, -m**: Specific model to use
+- **--verbose, -v**: Enable verbose logging
 - **--help**: Show help message
 
 ### Examples
 
 ```bash
-# Simple query
+# Simple query (orchestrated mode by default)
 agentic-data-scientist "What is Python?"
 
 # With file upload
 agentic-data-scientist "Analyze this data" --files data.csv
 
-# Use Claude Code
-agentic-data-scientist "Write a script" --agent claude_code
+# Use simple mode (direct Claude Code)
+agentic-data-scientist "Write a script" --mode simple
 
 # Stream output
 agentic-data-scientist "Complex task" --stream
 
-# Custom model
-agentic-data-scientist "Query" --model claude-sonnet-4-5-latest
+# Verbose logging
+agentic-data-scientist "Debug this" --verbose
 ```
 
 ## Environment Variables

docs/getting_started.md

@@ -51,14 +51,14 @@ CLAUDE_SCIENTIFIC_SKILLS_URL=https://mcp.k-dense.ai/claude-scientific-skills/mcp
 ### 2. Simple CLI Usage
 
 ```bash
-# Ask a simple question
+# Ask a simple question (uses orchestrated mode by default)
 agentic-data-scientist "What is quantum computing?"
 
 # Analyze a file
 agentic-data-scientist "Analyze this data" --files data.csv
 
-# Use Claude Code agent for coding tasks
-agentic-data-scientist "Write a Python script to parse CSV" --agent claude_code
+# Use simple mode (direct Claude Code without orchestration)
+agentic-data-scientist "Write a Python script to parse CSV" --mode simple
 
 # Stream responses for real-time output
 agentic-data-scientist "Complex analysis task" --stream

src/agentic_data_scientist/cli/main.py

@@ -26,32 +26,31 @@
 @click.command()
 @click.argument('query', required=False)
 @click.option('--files', '-f', multiple=True, help='Files to include in the query')
-@click.option('--agent', default='adk', type=click.Choice(['adk', 'claude_code']), help='Agent type to use')
-@click.option('--model', help='Model to use for the agent')
+@click.option('--mode', default='orchestrated', type=click.Choice(['orchestrated', 'simple']), 
+              help='Execution mode: "orchestrated" (default, with planning) or "simple" (direct Claude Code)')
 @click.option('--stream/--no-stream', default=False, help='Stream responses in real-time')
-@click.option('--mcp-servers', multiple=True, help='MCP servers to enable (filesystem, git, fetch, context7)')
 @click.option('--verbose', '-v', is_flag=True, help='Enable verbose logging')
 def main(
     query: Optional[str],
     files: tuple,
-    agent: str,
-    model: Optional[str],
+    mode: str,
     stream: bool,
-    mcp_servers: tuple,
     verbose: bool,
 ):
     """
-    Run Agentic Data Scientist agent with a query.
+    Run Agentic Data Scientist with a query.
+
+    MODE:
+        orchestrated (default): Full multi-agent system with planning, verification, and Claude Code implementation
+        simple: Direct Claude Code execution without orchestration
 
     Examples:
 
         agentic-data-scientist "What is machine learning?"
 
         agentic-data-scientist "Analyze this data" --files data.csv
 
-        agentic-data-scientist "Complex task" --agent claude_code --stream
-
-        agentic-data-scientist "Use git tools" --mcp-servers git --mcp-servers filesystem
+        agentic-data-scientist "Write a Python script" --mode simple --stream
     """
     # Set logging level
     if verbose:
@@ -74,13 +73,12 @@ def main(
         else:
             click.echo(f"Warning: File not found: {f}", err=True)
 
+    # Map mode to agent_type
+    agent_type = "adk" if mode == "orchestrated" else "claude_code"
+
     # Create core instance
     try:
-        core = DataScientist(
-            agent_type=agent,
-            model=model,
-            mcp_servers=list(mcp_servers) if mcp_servers else None,
-        )
+        core = DataScientist(agent_type=agent_type)
     except Exception as e:
         click.echo(f"Error initializing Agentic Data Scientist: {e}", err=True)
         sys.exit(1)