Advanced Claude Code Techniques: Mastering Tool Combinations and Workflows

Level up your AI-assisted coding with powerful combinations of Claude Code tools and optimization strategies.

Introduction

[!INFO]
In our previous post, we covered the basics of Claude Code and its core features. Now that you understand the fundamentals, it's time to explore advanced techniques that can dramatically enhance your productivity. Expert Claude Code users know that the real power lies not just in individual features, but in how they can be combined and optimized.

[!NOTE]
This second installment of our five-part series reveals how to use Claude Code like a pro: chaining tools together, working around limitations, optimizing performance, and implementing creative workflows that would be challenging to achieve with conventional development methods.

Advanced Use Cases At A Glance

Use Case	Key Tools	Benefits
Cascading Searches	Glob → Grep → Read → Task	Efficiently locate code patterns in large codebases
Write-Test-Fix Loop	MultiEdit → Bash → Grep	Rapid development with immediate feedback
Distributed Analysis	Multiple Task agents	Parallel processing of complex codebases
Agent Chain-of-Thought	Structured Task prompts	Enhanced reasoning and more accurate outputs
Inter-Agent Coordination	Multiple specialized agents	Complex workflows with domain-specific expertise

Advanced Tool Combinations

Cascading Searches for Efficient Code Navigation

One of the most powerful techniques in Claude Code is the cascading search pattern, which helps you quickly find and understand specific code in large codebases. Here's how it works:

1. Start with a broad Glob search to identify candidate files:

Glob(pattern: "**/*auth*.{js,ts}")

2. Narrow down with contextual Grep searches:

Grep(pattern: "function\\s+authenticate", 
     glob: "**/auth*.{js,ts}", 
     output_mode: "content", 
     -C: 2)

3. Read the most promising files in full:

Read(file_path: "/path/to/auth_service.ts")

4. For deeper analysis, employ a Task agent:

Task(description: "Analyze auth implementation",
     prompt: "Examine this authentication service and identify security best practices used or missing",
     subagent_type: "general-purpose")

[!TIP]
This progressive refinement approach is far more efficient than attempting to search everything at once or reading files blindly.

Write-Test-Fix Loop for Rapid Development

When implementing new features or fixing bugs, Claude Code excels at tight feedback loops:

1. Read the file needing modification:

Read(file_path: "/path/to/component.js")

2. Make multiple coordinated changes:

MultiEdit(file_path: "/path/to/component.js", edits: [
  {old_string: "// TODO: Add validation", 
   new_string: "// Validate user input\nif (!input || input.trim() === '') {\n  return setError('Input cannot be empty');\n}"},
  {old_string: "const [data, setData] = useState(null);", 
   new_string: "const [data, setData] = useState(null);\n  const [error, setError] = useState(null);"}
])

3. Immediately run tests to verify changes:

Bash(command: "npm test -- --filter=Component.test.js")

4. Grep for test failures if any:

Grep(pattern: "FAIL", path: "test-output.log")

5. Make additional edits to fix failing tests:

MultiEdit(file_path: "/path/to/component.js", edits: [
  {old_string: "return setError('Input cannot be empty');", 
   new_string: "setError('Input cannot be empty');\n  return;"}
])

This rapid iteration cycle significantly speeds up development compared to manual edit-test-fix cycles.

Distributed Code Analysis

For complex analysis tasks across large codebases, Claude Code can distribute work across multiple agents:

// Frontend analysis
Task(description: "Analyze frontend components", 
     prompt: "Find all React components that use user authentication", 
     subagent_type: "general-purpose")

// Backend analysis (run concurrently)
Task(description: "Analyze backend auth", 
     prompt: "Examine backend authentication implementation and security", 
     subagent_type: "general-purpose")

This parallel processing approach allows you to analyze different parts of your codebase simultaneously, then consolidate the findings for a comprehensive view.

Overcoming Tool Limitations

[!WARNING]
Even the most powerful tools have constraints. Here's how to work around common Claude Code limitations:

Handling Large Files

[!NOTE]
Claude Code's Read tool has a 2000 line limit, but you can work around this with several techniques:

1. Paginate through large files:

// Read first 1000 lines
Read(file_path: "/path/to/large_file.js", offset: 0, limit: 1000)

// Read next 1000 lines
Read(file_path: "/path/to/large_file.js", offset: 1000, limit: 1000)

2. Use Grep to find just the relevant sections:

Grep(pattern: "class DatabaseConnection", 
     path: "/path/to/large_file.js", 
     output_mode: "content",
     -C: 50)

3. Create a Bash helper function in your .bashrc or .zshrc:

read_chunk() {
  local file=$1
  local start=$2
  local chunk_size=${3:-1000}
  sed -n "${start},+${chunk_size}p" "$file"
}

Then use it via Bash:

Bash(command: "read_chunk /path/to/large_file.js 1500 500")

Enhanced Glob Patterns

When standard Glob patterns aren't enough:

# Find TypeScript files modified in the last week that contain 'auth'
Bash(command: "find ./src -name '*.ts' -mtime -7 -exec grep -l 'auth' {} \\;")

Working with Binary Files

For non-text files:

# Extract metadata from images
Bash(command: "exiftool -json ./assets/images/logo.png")

# Analyze dependencies in a compiled binary
Bash(command: "ldd /path/to/binary | grep 'not found'")

Performance Optimization Techniques

Context Management

Claude Code works within a context window limit. Optimize your context usage:

1. Use line numbers only when necessary:

# Instead of always using line numbers
Read(file_path: "/path/to/file.js")

# Only use when referencing specific lines
Grep(pattern: "function", path: "/path/to/file.js", output_mode: "content", -n: true)

2. Minimize context lines in Grep searches:

# Instead of
Grep(pattern: "auth", -C: 10, output_mode: "content")

# Use
Grep(pattern: "auth", output_mode: "files_with_matches")
Read(file_path: "/path/to/specific/file.js")

3. Use the head_limit parameter to restrict output size:

Grep(pattern: "error", path: "logs/*.log", output_mode: "content", head_limit: 20)

4. Clear completed todos to reduce context bloat:

TodoWrite(todos: [
  # Only include active and pending todos, remove completed ones
])

Parallel Execution

Claude Code can execute multiple tool calls in a single response, significantly improving performance:

# Instead of sequential reads:
# Read file1 -> wait -> Read file2 -> wait

# Use parallel reads:
[Read file1, Read file2] -> process both results at once

Similarly, for Bash commands:

# Run tests and linting in parallel
Bash(command: "npm run test")
Bash(command: "npm run lint")

Task Delegation Decision Tree

• Use Task agent when:
• Search requires multiple iterations or refinement
• You need deep analysis of multiple files
• The task requires specialized knowledge
• You're conducting open-ended exploration
• Use direct tools when:
• You have specific, known-path operations
• Context retention between operations is important
• You're making simple operations on few files

Advanced Agent Techniques

Agent Chain-of-Thought Optimization

One of the most powerful yet underutilized techniques in Claude Code is structuring prompts to follow explicit reasoning paths, known as chain-of-thought prompting. This significantly enhances the quality of agent outputs:

Task(description: "Security audit of authentication system",
     prompt: """
     Analyze the authentication system with the following structured approach:
     
     1. First, identify all authentication-related files by searching for patterns like 'auth', 'login', and 'session'
     2. Then, map the authentication flow by identifying function calls and data paths
     3. Next, evaluate token generation, validation, and storage mechanisms
     4. Specifically check for common security vulnerabilities:
        - CSRF protection
        - Token storage methods (cookies vs. localStorage)
        - Encryption of sensitive data
        - Session timeout and rotation policies
     5. Finally, provide a security assessment with recommendations prioritized by severity
     
     Present your findings as a structured security report with code references.
     """,
     subagent_type: "general-purpose")

This structured approach guides the agent through a logical sequence of reasoning steps, resulting in more thorough and accurate analysis than a simple "check the auth system for security issues" prompt.

Inter-Agent Coordination

Complex tasks often benefit from multiple specialized agents working together. Here's how to implement an architecture analysis workflow with coordinated agents:

# Step 1: Deploy multiple specialized agents in parallel
# Frontend architecture analysis
Task(description: "Analyze frontend architecture", 
     prompt: "Examine frontend code in src/frontend and document component hierarchy, state management patterns, and data flow between components. Focus on identifying reusable patterns and potential optimization points.",
     subagent_type: "general-purpose")

# Backend architecture analysis (runs concurrently)
Task(description: "Analyze backend architecture", 
     prompt: "Examine backend code in src/backend to map API structure, database interactions, authentication flow, and middleware usage. Identify performance bottlenecks and security concerns.",
     subagent_type: "general-purpose")

# Data model analysis (runs concurrently)
Task(description: "Analyze data models", 
     prompt: "Examine data models in src/models to document entity relationships, validation logic, and type definitions. Evaluate data integrity constraints and normalization.",
     subagent_type: "general-purpose")

# Step 2: Integrate findings with a coordination agent
Task(description: "Create comprehensive architecture overview", 
     prompt: "Synthesize the findings from the frontend, backend, and data model analyses to create an integrated architectural overview. Focus on cross-cutting concerns, interfaces between systems, and overall architectural patterns.",
     subagent_type: "general-purpose")

This approach allows Claude Code to divide and conquer complex tasks, with each agent focusing on its area of expertise before consolidating the results.

Security Auditing Workflow

Claude Code excels at security analysis when properly prompted. Here's a comprehensive security audit workflow:

Task(description: "Security vulnerability assessment",
     prompt: """
     Conduct a comprehensive security assessment of the codebase with these steps:
     
     1. First, locate sensitive operations by searching for:
       - Authentication code
       - Data validation functions
       - File operations
       - Network requests
       - Database queries
       - User input handling
     
     2. For each identified area:
       - Review implementation for common vulnerabilities
       - Check for proper input validation
       - Verify secure coding practices
       - Identify missing safeguards
     
     3. Specifically look for:
       - SQL injection possibilities
       - Cross-site scripting (XSS) opportunities
       - Authentication bypass vectors
       - Insecure direct object references
       - Missing authorization checks
       - Sensitive data exposure
       - Security misconfigurations
     
     4. Create a comprehensive report with:
       - Executive summary of findings
       - Detailed vulnerability descriptions with severity ratings
       - Code references for each issue
       - Recommended fixes with code examples
       - Prioritized remediation plan
     """,
     subagent_type: "general-purpose")

This specialized workflow transforms Claude Code into a powerful security auditing tool that can identify vulnerabilities that might be missed in manual code reviews.

Creative Use Cases

Automated Refactoring

Transform your codebase systematically:

1. Define a refactoring pattern (e.g., converting callbacks to promises)
2. Search for pattern instances:

Grep(pattern: "\\.then\\(function\\s*\\(", glob: "src/**/*.js")

3. Transform each instance:

MultiEdit(file_path: "/path/to/file.js", edits: [
  {old_string: "api.getUsers().then(function(users) {\n  renderUsers(users);\n});", 
   new_string: "try {\n  const users = await api.getUsers();\n  renderUsers(users);\n} catch (error) {\n  console.error(error);\n}"}
])

Codebase Visualization

Generate visual representations of code structure:

# Generate dependency graph
Bash(command: "find ./src -name '*.js' | xargs grep -l 'import' | xargs analyze-imports > deps.json")

This output can be visualized using web-based tools or further processed to create architecture diagrams.

Interactive Documentation Generation

Create living documentation directly from your code:

1. Extract JSDoc comments:

Grep(pattern: "/\\*\\*[\\s\\S]*?\\*/", glob: "src/**/*.js", output_mode: "content")

2. Parse function signatures:

Grep(pattern: "function\\s+\\w+\\s*\\([^)]*\\)", glob: "src/**/*.js", output_mode: "content")

3. Generate markdown documentation:

Write(file_path: "/docs/api-reference.md", content: "# API Reference\n\n...")

Custom Hooks for Enhanced Workflows

Create specialized processes for repetitive tasks with Claude Code hooks:

# ~/.config/claude-code/hooks.yaml
pre-write:
  - path: "*.js"
    run: "./scripts/lint.sh {{file}}"
post-edit:
  - path: "*/test/*.js"
    run: "npm test -- --filter={{file}}"

These hooks automate quality checks and testing, ensuring your code meets standards before it's even saved.

Data Science Workflow Integration

Claude Code's specialized tools make it particularly powerful for data science workflows. Here's an advanced example of exploratory data analysis:

# Step 1: Edit Jupyter notebook to load and explore data
NotebookEdit(
  notebook_path: "/path/to/analysis.ipynb", 
  cell_number: 0,
  new_source: """
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Load the dataset
df = pd.read_csv('data.csv')
df.head()
  """
)

# Step 2: Execute the code in the Jupyter kernel
mcp__ide__executeCode(code: """
# Examine dataset structure
print("Dataset shape:", df.shape)
print("\\nData types:\\n", df.dtypes)
print("\\nMissing values:\\n", df.isnull().sum())
print("\\nSummary statistics:\\n", df.describe())

# Create visualizations
plt.figure(figsize=(14, 6))

# Distribution plot
plt.subplot(1, 2, 1)
sns.histplot(df['age'], kde=True)
plt.title('Age Distribution')

# Correlation heatmap
plt.subplot(1, 2, 2)
sns.heatmap(df.select_dtypes(include=[np.number]).corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.tight_layout()
plt.show()
""")

# Step 3: Add model training code
NotebookEdit(
  notebook_path: "/path/to/analysis.ipynb", 
  cell_number: 3,
  edit_mode: "insert",
  cell_type: "code",
  new_source: """
# Prepare features and target
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

X = df.drop('target', axis=1)
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

# Feature importance
importances = pd.DataFrame({
    'feature': X.columns,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)

plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=importances[:10])
plt.title('Feature Importance')
plt.tight_layout()
plt.show()
"""
)

This workflow demonstrates how Claude Code can handle the entire data science process from exploration to model building, all within a Jupyter environment.

Real-World Advanced Workflow Example

Let's walk through a complete advanced workflow: refactoring an authentication system to use JWT tokens.

1. Research & Planning:

Task(description: "Research JWT best practices", 
     prompt: "Research current best practices for JWT authentication in Node.js applications, including token expiration, refresh strategies, and security considerations", 
     subagent_type: "general-purpose")

2. Codebase Analysis:

# Find all auth-related files
Glob(pattern: "**/*auth*.{js,ts}")

# Find token generation and validation
Grep(pattern: "(generate|validate|verify).*token", 
     glob: "**/*auth*.{js,ts}", 
     output_mode: "content")

3. Implementation Strategy:

TodoWrite(todos: [
  {content: "Update token generation to use JWT", status: "pending", activeForm: "Updating token generation to use JWT"},
  {content: "Implement token verification middleware", status: "pending", activeForm: "Implementing token verification middleware"},
  {content: "Add token refresh endpoint", status: "pending", activeForm: "Adding token refresh endpoint"},
  {content: "Update frontend authentication service", status: "pending", activeForm: "Updating frontend authentication service"},
  {content: "Write migration guide", status: "pending", activeForm: "Writing migration guide"}
])

4. Implementing Changes:

# Read current token generation
Read(file_path: "src/auth/tokenService.js")

# Update to use JWT
MultiEdit(file_path: "src/auth/tokenService.js", edits: [
  {old_string: "const generateToken = (userId) => {\n  return crypto.randomBytes(32).toString('hex');\n};", 
   new_string: "const generateToken = (userId) => {\n  return jwt.sign(\n    { userId },\n    process.env.JWT_SECRET,\n    { expiresIn: '1h' }\n  );\n};"},
  {old_string: "const verifyToken = (token) => {\n  // Check if token exists in database\n  return db.tokens.findOne({ token });\n};", 
   new_string: "const verifyToken = (token) => {\n  try {\n    return jwt.verify(token, process.env.JWT_SECRET);\n  } catch (error) {\n    console.error('Token verification failed:', error.message);\n    return null;\n  }\n};"}
])

5. Testing Changes:

Bash(command: "npm test -- --grep=\"token\"")

6. Deployment Preparation:

Write(file_path: "docs/jwt-migration-guide.md", content: "# JWT Migration Guide\n\n...")

This advanced workflow combines research, systematic codebase analysis, careful implementation, testing, and documentation — all orchestrated through Claude Code.

Conclusion

The advanced techniques covered in this guide represent the difference between using Claude Code as a mere coding assistant and leveraging it as a powerful force multiplier in your development workflow. By mastering tool combinations, working around limitations, optimizing performance, and implementing creative workflows, you can tackle complex development tasks with unprecedented efficiency.

In our next post, we'll explore how to configure Claude Code for your team, including deployment options, shell integrations, and customization strategies that ensure consistent, reliable performance across your organization.

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This is part 2 of a 5-part series on mastering Claude Code. Continue reading with Configuring Claude Code for Your Team, Specialized Workflows, and Enterprise CI/CD Integration, or revisit Getting Started with Claude Code.