I’ve just released a new open-source project that transforms how you work with Claude Code: claude-config-template. It’s a comprehensive configuration system that adds 11 specialized AI agents, 8 custom slash commands, orchestration capabilities for coordinating multiple agents, improved context awareness, and a structured documentation framework to your development workflow.

Why This Matters

If you’ve been following my writing, you know I believe strongly in combining AI capabilities with engineering discipline. Claude Code is powerful out of the box, but without structure and context, even the best AI tools can produce inconsistent results.

This configuration template solves that problem by:

  • Providing specialized agents that handle specific tasks (codebase analysis, architecture design, documentation research, etc.)
  • Enforcing structured workflows through custom slash commands
  • Making documentation a first-class citizen with dedicated templates and organized storage
  • Enabling parallel execution so you can run multiple agents simultaneously
  • Using git tree integration to give agents better context about your project structure
  • Coordinating complex tasks through orchestration capabilities that manage multiple specialized agents

What’s Included

11 Specialized AI Agents

Each agent is designed for a specific purpose:

Codebase Intelligence:

  • codebase-locator - Find WHERE code lives
  • codebase-analyzer - Understand HOW code works
  • codebase-pattern-finder - Discover similar implementations
  • codebase-researcher - Orchestrate comprehensive research

Architecture & Planning:

  • system-architect - Design systems and evaluate patterns
  • plan-implementer - Execute approved technical plans

Documentation Research:

  • project-context-analyzer - Extract and synthesize project documentation context
  • technical-docs-researcher - Search technical documentation
  • thoughts-analyzer - Deep dive into your thoughts directory
  • thoughts-locator - Find relevant documents

External Research:

  • web-search-researcher - Research from the web

8 Custom Slash Commands

These commands streamline common workflows:

  • /project - Create project documentation from templates
  • /research_codebase - Deep codebase investigation
  • /create_plan - Interactive implementation planning
  • /implement_plan - Execute approved plans
  • /validate_plan - Validate implementation plans
  • /commit - Create well-formatted git commits
  • /describe_pr - Generate comprehensive PR descriptions
  • /code_reviewer - Review code quality

Structured Documentation System

The template includes a complete documentation framework:

thoughts/
├── templates/
│   ├── project.md.template
│   ├── musthaves.md.template
│   ├── shouldhaves.md.template
│   ├── todo.md.template
│   └── epics.md.template
├── technical_docs/
└── shared/
    ├── plans/
    ├── research/
    └── project/
        └── epics/

This structure ensures that all documentation generated by AI agents is organized, version-controlled, and easily accessible. As I discussed in my post on faking a rational design process, documentation becomes the control mechanism for maintaining coherent design across all AI interactions.

Orchestration Agents

Not all tasks can be handled by a single specialized agent. That’s where orchestration agents come in. These meta-agents can:

  • Coordinate multiple agents - Break complex tasks into subtasks and assign them to appropriate specialized agents
  • Manage dependencies - Ensure agents run in the correct order when tasks depend on each other
  • Aggregate results - Combine outputs from multiple agents into coherent deliverables
  • Handle parallel execution - Run independent agents simultaneously for faster completion

For example, when implementing a new feature, an orchestration agent might:

  1. Use the codebase-researcher to investigate existing patterns
  2. Run the system-architect to evaluate design approaches
  3. Coordinate the plan-implementer to execute the approved plan

This allows you to tackle complex, multi-dimensional tasks without manually coordinating each step.

Real-World Workflow Example

Here’s how you might use the template in practice:

# 1. Document your project (one-time setup)
/project Create full docs for my e-commerce platform

# 2. Research existing implementation
/research_codebase payment processing flow

# 3. Create implementation plan
/create_plan add Stripe payment integration

# 4. Implement the plan
/implement_plan thoughts/shared/plans/2025-10-14-stripe-integration.md

# 5. Review the changes
/code_reviewer

# 6. Create commit
/commit

# 7. Generate PR description
/describe_pr

This workflow ensures you understand the codebase, plan carefully, implement systematically, and maintain quality throughout.

Getting Started

Installation is simple with a single command:

curl -fsSL https://raw.githubusercontent.com/albertsikkema/claude-config-template/main/install.sh | bash

The installation script will:

  • Copy all agents and commands to your project’s .claude/ directory
  • Create the thoughts/ documentation structure with templates
  • Set up pre-configured permissions in .claude/settings.local.json
  • Update your .gitignore to exclude configuration files

You can also use GitHub’s “Use this template” feature or clone and install manually. See the repository for all installation options.

The Workflow Pattern

The configuration enforces a proven workflow:

  1. Research - Understand the problem space
  2. Plan - Create a structured approach
  3. Implement - Execute with AI assistance

This maps closely to the PDCA cycle approach I’ve written about, bringing structure and repeatability to AI-assisted development. This isn’t about blindly letting AI generate code. It’s about using AI to augment your capabilities while maintaining control and quality.

Why I Built This

I’ve spent the past months exploring AI-assisted development for production systems. What I’ve learned is that raw AI capability isn’t enough—you need structure, context, and process.

This template builds on excellent work from the community, particularly:

The template embodies patterns that have proven effective in production:

  • Specialized intelligence - Separate agents for codebase analysis, documentation research, and external investigation
  • Structured planning - PRD-based approach ensures comprehensive implementation plans
  • Project memory - The thoughts structure maintains context across all AI sessions
  • Flexible invocation - Agents work automatically, on-demand, or in parallel as needed

What’s Next

This is version 1.0.0, and I’m actively developing new features based on real-world usage. The repository welcomes contributions from individuals and companies alike:

  • New agents and slash commands
  • Improved documentation and examples
  • Bug fixes and enhancements
  • Installation script improvements
  • Templates and workflow ideas

If you have ideas for new agents, commands, or workflow improvements, check out the CONTRIBUTING.md guide. For questions or larger contributions, you can reach me at license@albertsikkema.com.

Key Features at a Glance

What makes this template different from basic Claude Code configurations:

  • Complete workflow system - Not just tools, but proven processes
  • Intelligent automation - Agents that research and coordinate autonomously
  • Structured knowledge - Organized documentation with templates
  • Battle-tested patterns - Workflows refined through production use
  • Easy to customize - Extend and adapt to your specific needs
  • Safe installation - Preserves existing work, updates only configuration

Try It Out

The best way to understand the value is to try it in your own projects. After installation, restart Claude Code and try:

  1. /project to document your project
  2. /research_codebase to investigate a feature
  3. /create_plan to plan an enhancement
  4. /implement_plan to execute your plan

Visit the repository: github.com/albertsikkema/claude-config-template

The goal isn’t to replace human thinking—it’s to create a framework where AI and human expertise combine effectively. Where every interaction is guided by structure, every output is documented, and every decision is intentional.

That’s the kind of AI-assisted development that actually works in production.

What’s your experience with AI coding assistants? Have you found ways to add structure to your workflows? I’d love to hear about it—connect with me on LinkedIn.