Mastering Claude Code: The Complete Configuration Guide

Why Configuration Matters 🎯
The claude-conf Repository 📦
Core Configuration Files ⚙️
Specialized Agents 🤖
Custom Slash Commands ⚡
MCP Integration: all-in-mcp 🔌
Claude Code v2.x Essential Features 💡
Real-World Workflows 🌟
Advanced Tips and Best Practices 🎓
Conclusion: Your Optimized Development Environment 🎯
Related Resources 📖

Why Configuration Matters 🎯

Claude Code is powerful out of the box, but its true potential unlocks when you configure it for your specific workflow. Think of it like moving from a basic text editor to a fully customized IDE – the difference is transformative.

A well-configured Claude Code setup fundamentally changes how you interact with AI assistance. Instead of a general-purpose chatbot, you get specialized AI assistants purpose-built for specific tasks like academic research, blog writing, or Git workflows. These agents understand the nuances of their domain and deliver consistently high-quality results.

Beyond specialized agents, custom configuration brings you quick-access shortcuts through slash commands that trigger complex workflows with a single keystroke. Need to search multiple academic databases simultaneously? There's a command for that. Want to generate a professional weekly report from your rough notes? One command handles it all.

The real game-changer comes from external integrations through MCP (Model Context Protocol) servers. These connect Claude Code to academic databases like IACR and Google Scholar, GitHub repositories, and other external tools – dramatically expanding what's possible without ever leaving your development environment. Finally, with standardized workflows built into your configuration, you ensure consistent quality and save countless hours across all your projects.

This guide walks you through building a complete Claude Code configuration ecosystem, from basic settings to advanced academic research capabilities. By the end, you'll have a personalized AI development environment that feels like working with a team of expert specialists.

The claude-conf Repository 📦

The claude-conf repository provides a production-ready configuration template specifically designed for academic and technical workflows. It's not just a collection of files – it's a complete ecosystem of specialized tools.

Project Structure

.claude/
├── agents/              # Specialized AI assistants
│   ├── academic-search.md
│   ├── blog-writing.md
│   ├── commit-push.md
│   ├── paper-research.md
│   └── weekly-report-writing.md
├── commands/            # Custom slash commands
│   ├── find-paper.md
│   ├── search-papers.md
│   ├── weekly-report.md
│   └── wpaper.md
├── settings.json        # Core configuration
└── .mcp.json           # MCP server setup

This structure follows Claude Code's configuration conventions while organizing tools by function. The separation is intentional and powerful: Agents handle complex, multi-step tasks autonomously, making intelligent decisions and using multiple tools to accomplish their goals. Commands, on the other hand, provide quick-access shortcuts for common operations where you want standardized, repeatable behavior.

Key Features

The claude-conf configuration centers around three core pillars that cover the most common professional workflows. For academic research 🎓, you get systematic paper discovery across IACR, Google Scholar, CryptoBib, and Crossref databases with built-in quality verification and peer-review filtering. This means you're not just finding papers – you're finding high-quality, vetted research that meets academic standards.

The content creation ✍️ capabilities transform your writing workflow with professional blog writing assistance, academic report generation, and technical documentation tools. Each writing agent understands proper formatting conventions, academic language standards, and how to structure content for maximum readability and impact.

Finally, development workflows 🔧 are streamlined through automated Git operations, intelligent code management, and organized project structures. From writing standardized commit messages to managing changelogs, these tools ensure your development practices remain consistent and professional.

Core Configuration Files ⚙️

Before diving into specialized agents and custom commands, let's understand the foundation of your Claude Code setup. Two core files control everything: settings.json for Claude's behavior and .mcp.json for external tool integration.

settings.json

Your settings.json file is the control center for Claude Code's core behavior. It determines what operations Claude can perform automatically, which require confirmation, and how Claude interacts with external tools. Here's the claude-conf configuration:

{
  "permissions": {
    "allow": ["Bash(git add:*)"],
    "deny": [],
    "ask": []
  },
  "enableAllProjectMcpServers": true,
  "env": {},
  "includeCoAuthoredBy": true
}

Key Settings Explained:

The permissions object is your security layer, controlling which operations Claude Code can perform without asking. It has three arrays: allow contains pre-approved operations (here, git staging operations that are safe and reversible), deny lists explicitly blocked operations for sensitive commands you never want automated, and ask defines operations that require your confirmation before execution. This granular control ensures Claude helps efficiently while maintaining safety guardrails.

Setting enableAllProjectMcpServers to true automatically activates all MCP servers defined in your .mcp.json file when you start Claude Code. This means your academic research tools, GitHub integration, and other external capabilities are ready to use immediately without manual activation.

The includeCoAuthoredBy flag adds Claude as a co-author to your Git commits, which is both transparent and often required by academic integrity policies. When enabled, every commit includes a "Co-Authored-By: Claude" line in the commit message, making it clear that AI assistance was used in the development process.

For complete settings documentation, see the official Claude Code settings reference.

.mcp.json

The .mcp.json file configures Model Context Protocol servers – external tools that extend Claude Code's capabilities:

{
  "mcpServers": {
    "all-in-mcp": {
      "type": "stdio",
      "command": "pipx",
      "args": ["run", "all-in-mcp"],
      "env": {
        "APAPER": "true",
        "GITHUB_REPO_MCP": "true"
      }
    }
  }
}

This configuration enables the all-in-mcp server (covered in detail below), which provides academic paper search and GitHub repository tools.

Specialized Agents 🤖

Now we enter the heart of the claude-conf system: specialized agents. These are autonomous AI assistants designed for specific tasks, each with focused expertise, custom tool access, and optimized prompts for their domain. Think of them as having a team of specialists on call – a research librarian, a professional editor, a Git workflow expert – each understanding exactly what you need in their area of expertise.

1. Blog-Writing Agent ✍️

Purpose: Transform draft content into polished, publication-ready blog posts

When to Use:

Drafts feel incomplete or choppy
Bullet points need to flow as paragraphs
Content needs formatting and readability enhancement

Key Capabilities:

The blog-writing agent excels at transforming rough, disconnected notes into cohesive, flowing paragraphs that keep readers engaged from start to finish. It strategically employs bold text for key concepts, italics for emphasis, and carefully selected emojis 📝 to add personality without overwhelming the content. Beyond formatting, it enriches your posts by adding relevant reference links and authoritative resources that support your claims, while ensuring smooth transitions between ideas and crafting compelling conclusions that leave readers with clear takeaways.

Example:

claude "This tutorial feels choppy. Help me make it flow better."
# Automatically triggers blog-writing agent

# Or explicitly:
/agents blog-writing "Polish this React hooks draft"

2. Paper-Research Agent 📚

Purpose: Systematic academic paper discovery with rigorous quality verification

When to Use:

Literature review for research projects
Finding high-quality papers on specific topics
Cryptographic research requiring peer-reviewed sources

Key Capabilities:

Searches IACR, CryptoBib, Google Scholar, and Crossref
Applies peer-review and citation quality filters
Deep analysis of selected papers
Extracts key contributions and methodologies

Research Process:

The agent follows a systematic four-stage methodology that mirrors academic research best practices. It begins with primary search across IACR ePrint and specialized cryptography databases, casting a wide net to find relevant work. Next comes relevance assessment, where it filters papers based on topical alignment, recency, and citation patterns. The critical quality verification step cross-references findings with CryptoBib to ensure peer-review status and academic rigor. Finally, it performs deep analysis on selected papers, extracting key findings, methodologies, and implications for your research.

Example:

/agents paper-research "Find recent papers on zero-knowledge proofs"

3. Academic-Search Agent 🔍

Purpose: Comprehensive paper discovery across multiple platforms

When to Use:

Broad topic exploration across disciplines
Need citation counts and metadata
Multi-platform search for thorough coverage

Difference from Paper-Research:

These two agents serve complementary purposes in your research workflow. The academic-search agent emphasizes breadth, casting a wide net across multiple platforms to give you comprehensive coverage of a topic – perfect for the initial exploration phase. In contrast, the paper-research agent focuses on depth, applying rigorous quality filters and verification to ensure every paper meets high academic standards – ideal when you need thoroughly vetted sources for your own research or citations.

Example:

/agents academic-search "Machine learning in cybersecurity papers"

4. Weekly-Report-Writing Agent 📊

Purpose: Professional progress reports with academic language

When to Use:

Research progress documentation
Technical achievement summaries
Formal status updates

Key Features:

This agent transforms casual notes and informal progress updates into polished, professional reports using clear and concise academic language. It knows how to structure technical achievements for maximum impact, strategically highlighting key accomplishments while maintaining appropriate formality. The result is a well-organized report with proper paragraph flow that you can confidently submit to advisors, supervisors, or research groups.

Example:

/agents weekly-report-writing "Write report on GPU profiling work"

5. Commit-Push Agent 🚀

Purpose: Complete Git workflow from staging to remote push

When to Use:

Finished feature implementations
Bug fixes ready to commit
Need consistent commit messages

Key Features:

The commit-push agent handles the complete Git workflow from start to finish, ensuring professional version control practices. It generates standardized commit messages following conventional commit formats with meaningful emojis (✨ for features, 🐛 for fixes, 📝 for documentation), making your Git history instantly readable. Beyond commits, it manages changelog updates, handles the complete stage-commit-push sequence, and performs smart categorization of changes with thorough git status verification at each step.

Example:

claude "Added user authentication, ready to commit"
# Triggers commit-push agent automatically

Custom Slash Commands ⚡

While agents handle complex autonomous tasks with multiple decision points, slash commands serve a different but equally important role: they provide instant, standardized shortcuts for common operations you perform repeatedly. Think of agents as consultants who analyze and adapt, while commands are macros that execute predefined workflows consistently every time. They're defined in .claude/commands/ as simple markdown files.

Command Structure

---
name: command-name
description: What this command does
---

# Your command's prompt/instructions
You are a specialized assistant for [specific task]...

Available Commands

/search-papers - Multi-platform academic paper search

/search-papers cryptographic protocols
# Searches IACR, Google Scholar, Crossref, and CryptoBib

/find-paper - Targeted paper retrieval

/find-paper "Homomorphic Encryption Survey"

/wpaper - Academic writing with rigorous standards

/wpaper methodology for neural network experiment

/weekly-report - Generate professional progress reports

/weekly-report completed GPU profiling optimization

Commands vs Agents

Understanding when to use commands versus agents is crucial for workflow efficiency. Reach for commands when you need simple, single-step operations with predictable behavior – like searching for papers across specific databases, generating a standard report format, or executing a quick query. Commands excel when you want standardized output that's consistent every time.

Turn to agents when your task involves multi-step workflows that require adaptive thinking, quality verification processes that need judgment calls, or complex decision-making where the AI needs to analyze context and choose the best approach. Agents bring intelligence and flexibility to tasks that can't be reduced to a simple script.

MCP Integration: all-in-mcp 🔌

The all-in-mcp repository is a FastMCP-based Model Context Protocol server that dramatically extends Claude Code's capabilities with academic research and GitHub integration tools.

Architecture

All-in-mcp employs a modular proxy architecture that acts as a smart intermediary between Claude Code and specialized tool modules:

Claude Code → all-in-mcp Proxy → Specialized Modules
                                ├── APaper (academic tools)
                                └── GitHub-Repo-MCP (repo tools)

This elegant design brings several key advantages. First, it enables better modularity and maintainability by keeping each tool set isolated and independently updatable. You get selective module activation through environment variables, meaning you only load the capabilities you actually need for each project. Finally, it creates isolated tool namespaces that prevent naming conflicts – critical when multiple modules might have similar tool names.

Available Tools

Tool	Description	Module
`search-iacr-papers`	Search IACR ePrint Archive	APaper
`download-iacr-paper`	Download IACR paper PDFs	APaper
`read-iacr-paper`	Extract text from IACR PDFs	APaper
`search-cryptobib-papers`	Search CryptoBib database	APaper
`search-google-scholar-papers`	Cross-discipline paper search	APaper
`search-crossref-papers`	Search with DOI metadata	APaper
`read-pdf`	Extract text from any PDF	APaper
`getRepoAllDirectories`	List GitHub repo structure	GitHub-Repo
`getRepoDirectories`	Get specific directory contents	GitHub-Repo
`getRepoFile`	Read GitHub file contents	GitHub-Repo

APaper Module Deep Dive

The APaper module is the powerhouse for academic research, providing comprehensive tools that rival dedicated research software.

🔍 Multi-Database Search:

The module connects you to four essential academic databases, each serving a distinct purpose. IACR ePrint gives you access to the premier cryptography research venue with pre-prints and published papers. CryptoBib provides quality-verified cryptographic bibliography data, ensuring you're citing peer-reviewed work. Google Scholar offers broad cross-discipline coverage with citation counts and metadata, perfect for understanding a paper's impact. Finally, Crossref delivers DOI-based academic publication metadata, giving you authoritative publication information and cross-references.

📄 PDF Processing:

Beyond search, APaper handles the entire paper lifecycle. You can download papers directly from IACR without leaving your terminal, extract text from both local and remote PDFs for analysis, and leverage Claude's capabilities for content analysis and summarization. This means going from paper discovery to deep understanding in a single, seamless workflow.

Academic Workflow Example:

# 1. Search for papers
/agents academic-search "lattice-based cryptography"

# 2. Verify quality through CryptoBib
# (Agent automatically cross-references)

# 3. Download and analyze top papers
# (Agent handles full-text extraction and analysis)

# 4. Generate literature review summary
# Result: Comprehensive analysis with citations

Installation and Setup

Prerequisites:

Python 3.10 or higher
pipx for isolated Python package installation

Installation:

# Install via pipx
pipx install all-in-mcp

# Or run directly without installation
pipx run all-in-mcp

Configuration:

Add to your .mcp.json:

{
  "mcpServers": {
    "all-in-mcp": {
      "type": "stdio",
      "command": "pipx",
      "args": ["run", "all-in-mcp"],
      "env": {
        "APAPER": "true",
        "GITHUB_REPO_MCP": "true"
      }
    }
  }
}

Environment Variables:

APAPER=true - Enables academic research tools
GITHUB_REPO_MCP=true - Enables GitHub repository tools

Testing Your Setup:

# Test with MCP Inspector
APAPER=true npx @modelcontextprotocol/inspector pipx run all-in-mcp

The MCP Inspector provides an interactive interface to test tools, view schemas, and debug server communication.

Claude Code v2.x Essential Features 💡

With your custom configuration in place, you'll also want to leverage the powerful built-in features that Claude Code v2.x brings to the table. These native capabilities complement your custom agents and commands, creating a complete development environment.

Built-in Slash Commands

Development Commands:

/agents - Manage and invoke custom agents
/mcp - Manage MCP server connections
/review - Request code review
/permissions - View/update permissions
/context - Show token usage statistics

Project Commands:

/init - Initialize project with CLAUDE.md guide
/memory - Edit CLAUDE.md memory files
/add-dir - Add additional working directories

Session Commands:

/clear - Clear conversation history
/compact - Compact conversation with focus
/rewind - Rewind conversation/code changes

System Commands:

/config - Open Settings interface
/status - Show version, model, account
/doctor - Check installation health
/help - Get usage help

Utility Commands:

/model - Select or change AI model
/cost - Show token usage statistics
/terminal-setup - Install key binding
/vim - Enter vim mode

For complete documentation, see the official slash commands reference.

Creating Custom Commands

Custom commands live in .claude/commands/ and support:

Argument Placeholders:

$ARGUMENTS - All arguments as single string
$1, $2, etc. - Individual positional arguments

File References:

Search for papers about $1 and summarize findings.
Include related work from @references.md

Frontmatter Metadata:

---
name: my-command
description: Command purpose
model: sonnet
thinking: extended
---
Command prompt here...

Bash Execution:

---
name: run-tests
---
Execute the following bash commands:
\`\`\`bash
pytest tests/ --verbose
\`\`\`

MCP-Based Slash Commands

When MCP servers are connected, their tools automatically become available as slash commands:

Format: /mcp__<server-name>__<tool-name>

Example with all-in-mcp:

/mcp__all-in-mcp__search-iacr-papers quantum computing

/mcp__all-in-mcp__getRepoFile https://github.com/user/repo README.md

Real-World Workflows 🌟

Theory is important, but the real value emerges when you see how these tools work together in actual practice. Let's walk through three common scenarios that demonstrate the power of a well-configured Claude Code environment.

Academic Research Workflow

Scenario: Literature review for a cryptography research project

# 1. Broad exploration
/agents academic-search "post-quantum cryptography lattice-based"

# 2. Quality-verified deep dive
/agents paper-research "lattice cryptography NIST finalists"

# 3. Direct paper access
/mcp__all-in-mcp__download-iacr-paper 2024/123

# 4. Analysis and note-taking
/mcp__all-in-mcp__read-iacr-paper 2024/123
# Claude analyzes and summarizes key findings

# 5. Weekly progress report
/agents weekly-report-writing "Summarize this week's literature review progress"

Result: Comprehensive, quality-verified research with professional documentation.

Blog Writing Workflow

Scenario: Transform technical notes into engaging blog post

# 1. Draft initial content
claude "I have technical notes about implementing OAuth. Help me structure a blog post."

# 2. Polish with blog agent
/agents blog-writing "Transform these notes into an engaging tutorial"

# Result: Professional blog post with:
# - Flowing paragraphs (not bullet points)
# - Strategic formatting (bold, italics, emojis)
# - Reference links to OAuth specs
# - Smooth transitions and proper conclusion

Development + Documentation Workflow

Scenario: Complete feature with proper Git practices

# 1. Implement feature
claude "Add user authentication with JWT tokens"

# 2. Review implementation
/review

# 3. Write documentation
/agents blog-writing "Document the new authentication system"

# 4. Commit with standards
/agents commit-push "User authentication feature complete"

# Result:
# - ✨ feat: add JWT-based user authentication
# - Updated changelog.txt
# - Pushed to remote
# - Co-authored by Claude

Advanced Tips and Best Practices 🎓

Once you're comfortable with the basics, these advanced techniques will help you squeeze even more value from your configuration.

Agent Design Principles

Focus on Specificity: The narrower your agent's scope, the better it performs. A broadly-defined agent like "code-helper" tries to do everything and ends up mediocre at all of it. Instead, create specific agents like "api-documentation-generator" that excel at one particular task. Specificity drives expertise.

Curate Tool Access: Resist the temptation to give every agent access to every tool. Each agent should have only the tools it actually needs for its specific role. For example:

---
tools: Read, Write, Grep  # Just what this agent needs
---

This limitation isn't a restriction – it's a feature that keeps agents focused and prevents them from going down unproductive rabbit holes.

Define Clear Triggers: Help Claude understand exactly when to invoke each agent by providing explicit trigger conditions in the description:

description: Use when user explicitly mentions "literature review" or "find papers"

Clear triggers mean the right specialist gets called for each job, without you having to manually specify agents every time.

Command Optimization

Use Arguments Effectively:

Search for papers about $1 published after $2
# Usage: /search-papers "blockchain" "2023"

Combine with File References:

Analyze @current-file.py and suggest optimizations based on @best-practices.md

MCP Server Management

Selective Activation: Enable only needed modules

"env": {
  "APAPER": "true",        // Academic tools
  "GITHUB_REPO_MCP": "false"  // Disable if not needed
}

Performance Optimization: Use MCP Inspector to test tools before production use

npx @modelcontextprotocol/inspector pipx run all-in-mcp

Security Considerations

Security should be a primary concern when configuring AI assistance, especially when granting automated permissions.

Permission Control: Always be explicit and conservative about allowed operations. The permission system is your safety net – use it wisely:

{
  "permissions": {
    "allow": [
      "Bash(git add:*)",    // Safe: staging is reversible
      "Read(*)"             // Safe: read operations don't modify
    ],
    "deny": [
      "Bash(rm:*)",        // Dangerous: file deletion
      "Bash(sudo:*)"       // Dangerous: elevated privileges
    ]
  }
}

Start with minimal permissions and expand only as needed. It's easier to grant more permissions later than to recover from an accidentally destructive operation.

Environment Variables: Never commit sensitive credentials to your configuration repository. Use placeholders and document what needs to be filled in:

"env": {
  "API_KEY": "${YOUR_API_KEY}"  // User must replace this
}

Consider using environment variables from your shell or a .env file that's properly git-ignored for sensitive values.

Conclusion: Your Optimized Development Environment 🎯

A well-configured Claude Code setup isn't just about convenience – it's about fundamentally transforming how you work. Think about the difference between using a generic search engine versus having a research librarian who knows exactly which databases to query and how to verify source quality. That's the leap you make with proper configuration.

The claude-conf ecosystem delivers this transformation through specialized expertise with five purpose-built agents covering research, writing, and development workflows. You get instant access through custom slash commands that execute complex operations with a single keystroke. Extended capabilities come through MCP integration, connecting you to academic databases like IACR and Google Scholar, plus GitHub repositories and other external tools. For academic work specifically, you gain complete research power from initial paper discovery through rigorous quality verification. Perhaps most importantly, standardized processes ensure consistent, professional results every single time you work.

This configuration paradigm represents a fundamental shift – from AI as a general-purpose tool to a specialized team of expert assistants, each optimized for specific aspects of your workflow. It's the difference between having a Swiss Army knife and having a fully-equipped workshop with specialized tools for every job.

Next Steps

Your journey to an optimized Claude Code environment starts now:

Clone and customize the claude-conf repository for your specific needs
Install all-in-mcp to unlock powerful academic research capabilities
Create custom agents for your unique workflows and specialized domains
Share your configurations with the community – help others build on your work

The examples in this guide are just the starting point. The real power comes from adapting these tools to your specific requirements and building a configuration that feels like a natural extension of your development process. Every researcher, developer, and writer has unique needs – your configuration should reflect that uniqueness.

Your optimized Claude Code environment awaits – start building it today, and experience what it's like to work with a team of AI specialists who truly understand your workflow!

Want to dive deeper into specific aspects of Claude Code? Check out these related guides:

Claude Code Subagents - Deep dive into the subagent architecture, specialization benefits, and creating custom agents
DeepSeek Meets Claude Code - Run Claude Code with DeepSeek V3 for 68x lower costs while maintaining full functionality

Official Documentation:

Repositories:

claude-conf Configuration - Complete configuration template
all-in-mcp Server - Academic research MCP server

Why Configuration Matters 🎯

The claude-conf Repository 📦

Project Structure

Key Features

Core Configuration Files ⚙️

settings.json

.mcp.json

Specialized Agents 🤖

1. Blog-Writing Agent ✍️

2. Paper-Research Agent 📚

3. Academic-Search Agent 🔍

4. Weekly-Report-Writing Agent 📊

5. Commit-Push Agent 🚀

Custom Slash Commands ⚡

Command Structure

Available Commands

Commands vs Agents

MCP Integration: all-in-mcp 🔌

Architecture

Available Tools

APaper Module Deep Dive

Installation and Setup

Claude Code v2.x Essential Features 💡

Built-in Slash Commands

Creating Custom Commands

MCP-Based Slash Commands

Real-World Workflows 🌟

Academic Research Workflow

Blog Writing Workflow

Development + Documentation Workflow

Advanced Tips and Best Practices 🎓

Agent Design Principles

Command Optimization

MCP Server Management

Security Considerations

Conclusion: Your Optimized Development Environment 🎯

Next Steps

Related Resources 📖