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Claude Code Configuration

Personal Claude Code configuration directory featuring 83+ specialized AI agents, custom skills, slash commands, GitHub workflow automation, and multi-agent orchestration patterns.

Table of Contents

Overview

This repository extends Claude Code with:

  • 83+ Specialized AI Agents across Haiku/Sonnet/Opus model tiers for domain-specific expertise
  • 10 Custom Skills for specialized tasks (financial analysis, WebGL development, Claude Code customization)
  • 13 Slash Commands for GitHub workflows, architecture, requirements, research, and content generation
  • 3 Development Methodologies — Issue-Driven, BDD, and Epic-Driven (Agent Teams)
  • Multi-Agent Orchestration patterns for complex development workflows
  • Session Persistence across projects and shell environments

Development Methodologies

This configuration supports three progressive development approaches, each building on the previous.

1. Individual: Issue-Driven Development

Flow: /issue -> /task

The most direct approach. Define features as structured GitHub issues with acceptance criteria, then execute with agent orchestration.

graph LR
    A[Feature Idea] -->|/issue| B[Epic + Sub-Issues]
    B -->|/task| C[Agent Orchestration]
    C -->|/pr| D[Pull Request]
    D -->|Review| E[Merge]

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#ffe1f0
    style E fill:#e1ffe1
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/issue "Add payment processing"
# -> Epic #100 + Sub-issues #101, #102, #103
#    with acceptance criteria, story points, agent assignments, dependency graphs

/task #101
# -> Agents implement, test, validate

/pr
# -> PR linked to #101

Best for: Technical features where requirements are clear and you want fast decomposition-to-implementation.


2. Behavioral: Behavior-Driven Development (BDD)

Flow: /user-story -> /issue -> /task

Starts from user behavior, formalizes it in Gherkin syntax, then decomposes and implements.

graph LR
    A[User Behavior] -->|/user-story| B[BDD Spec + Gherkin]
    B -->|/issue| C[Epic + Sub-Issues]
    C -->|/task| D[Agent Orchestration]
    D -->|/pr| E[Pull Request]
    E -->|Review| F[Merge]

    style A fill:#e1f5ff
    style B fill:#f0e1ff
    style C fill:#fff4e1
    style D fill:#ffe1f0
    style F fill:#e1ffe1
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/user-story
# -> Defines persona, goal, benefit
# -> Creates Gherkin scenarios (Given/When/Then)
# -> Sets semantic version target
# -> Creates GitHub issue with BDD specification

/issue "Implement user authentication"
# -> Decomposes BDD story into implementable sub-issues
# -> Maps scenarios to acceptance criteria

/task #124
# -> Agents implement against Gherkin scenarios
# -> Validates behavior matches specification

Best for: User-facing features where behavior must be formalized before implementation, or when working with non-technical stakeholders.

Full BDD Workflow (Detailed)

graph TD
    A[User Story] -->|/user-story| B[BDD Specification]
    B -->|Gherkin Scenarios| C[Epic Creation]
    C -->|/issue| D[Epic Parent Issue]

    D -->|Decompose| E1[Sub-Issue 1: Backend API]
    D -->|Decompose| E2[Sub-Issue 2: Frontend UI]
    D -->|Decompose| E3[Sub-Issue 3: Database Schema]
    D -->|Decompose| E4[Sub-Issue 4: Integration Tests]

    E1 -->|/task| F1[Backend Tasks]
    E2 -->|/task| F2[Frontend Tasks]
    E3 -->|/task| F3[Database Tasks]
    E4 -->|/task| F4[Testing Tasks]

    F1 -->|Agent: backend-architect| G1[API Implementation]
    F2 -->|Agent: frontend-developer| G2[UI Implementation]
    F3 -->|Agent: database-optimizer| G3[Schema Migration]
    F4 -->|Agent: test-automator| G4[Test Suite]

    G1 -->|/pr| H1[Backend PR]
    G2 -->|/pr| H2[Frontend PR]
    G3 -->|/pr| H3[Database PR]
    G4 -->|/pr| H4[Testing PR]

    H1 -->|Review: security-auditor| I1[Security Check]
    H2 -->|Review: code-reviewer| I2[Code Quality]
    H3 -->|Review: database-admin| I3[Schema Validation]
    H4 -->|Review: test-automator| I4[Coverage Check]

    I1 & I2 & I3 & I4 -->|Merge| J[Integration Branch]
    J -->|Final Review| K[Production Deploy]

    style A fill:#e1f5ff
    style D fill:#fff4e1
    style E1 fill:#f0f0ff
    style E2 fill:#f0f0ff
    style E3 fill:#f0f0ff
    style E4 fill:#f0f0ff
    style G1 fill:#ffe1f0
    style G2 fill:#ffe1f0
    style G3 fill:#ffe1f0
    style G4 fill:#ffe1f0
    style K fill:#e1ffe1
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Workflow Phases:

  1. User Story Phase (/user-story) — BDD specification with Gherkin scenarios and semantic versioning
  2. Epic Creation Phase (/issue) — Decomposition into sub-issues with dependencies, story points, and agent assignments
  3. Task Distribution Phase (/task) — Agent assignment with context isolation per sub-issue
  4. Implementation Phase — Parallel agent execution with domain-specific focus
  5. Pull Request Phase (/pr) — One PR per sub-issue with convention analysis
  6. Review Phase — Specialized validation agents (security, code quality, schema, performance)
  7. Integration & Deploy — Merge validated PRs and deploy

3. Scaled: Epic-Driven Development (Agent Teams)

Flow: /work-on-opens (wraps /task + /pr internally)

The scaled approach. Processes entire priority boards of epics using CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS, git worktrees for true parallel execution, and tier-based dependency resolution.

graph TD
    A[Priority Board] -->|/work-on-opens| B{Epic Queue}
    B -->|P0| C1[Epic 1]
    B -->|P1| C2[Epic 2]
    B -->|P2| C3[Epic 3]

    C1 -->|Clarify 3rd-party| D1[Sub-Issues]
    D1 -->|Dependency Graph| E1[Tier 0: Parallel]
    E1 -->|Git Worktrees| F1[Agent Team]
    F1 -->|/task + /pr| G1[PRs Created]
    G1 -->|Tier 1| H1[Next Tier]
    H1 -->|Complete| I1[Epic Done]

    I1 --> C2
    C2 --> D2[...]

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style E1 fill:#f0f0ff
    style F1 fill:#ffe1f0
    style I1 fill:#e1ffe1
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/work-on-opens <project-board-url>
# -> Fetches priority board (P0 > P1 > P2)
# -> For each epic:
#    1. Clarifies third-party integrations
#    2. Builds dependency graph of sub-issues
#    3. Groups into parallelizable tiers
#    4. Creates git worktrees per sub-issue
#    5. Runs /task in parallel (background agents)
#    6. Creates PRs via /pr as sub-issues complete
#    7. Reports recommended merge order
# -> Moves to next epic by priority

Best for: Batch execution of a backlog, sprint-level throughput, or when multiple epics need resolution with maximum parallelism.


Methodology Progression

Each approach builds on the previous:

Individual            Behavioral              Scaled
/issue -> /task       /user-story ->          /work-on-opens
                      /issue -> /task           (wraps /task + /pr)
                                                (agent teams)
                                                (git worktrees)

Complexity:  Low           Medium                   High
Parallelism: Single        Single                   Multi-epic, multi-agent
Input:       Feature       User behavior            Priority board
Output:      1 PR          1 PR per sub-issue       N PRs across M epics

Key Benefits Across All Methodologies:

  • Context Management: Sub-issues keep token usage manageable
  • Parallel Work: Multiple agents work simultaneously
  • Clear Dependencies: Mermaid graphs show integration points
  • Quality Gates: Each PR gets specialized review
  • Progress Tracking: Parent issue shows overall completion
  • Specialization: Right expert for each component

Supporting Commands

Two additional commands support the planning phase before entering any methodology:

  • /architecture — Define technology stacks, domain separation, and interconnection patterns before implementation
  • /mvp-requirements — Explore technical capabilities through documentation (NotebookLM) and define MVP scope

These feed into any of the three flows above by producing requirements and architecture documents that /issue and /user-story can reference.

Features

🤖 Specialized AI Agents

Collection of specialized domain-specific subagents from the wshobson/agents repository (included as git submodule).

Agents are optimized across Claude model tiers (Haiku/Sonnet/Opus) based on task complexity, covering:

  • Architecture & Design: System design, cloud infrastructure, API architecture
  • Programming Languages: Language-specific specialists for systems, web, enterprise, and mobile development
  • Infrastructure & Operations: DevOps, database management, networking
  • Security & Quality: Code review, security auditing, testing, performance engineering
  • AI/ML & Data: LLM applications, ML pipelines, data analysis
  • Documentation & Business: Technical writing, legal, HR, marketing

📖 See agents/README.md for:

  • Complete agent catalog with capabilities
  • Model distribution and selection guides
  • Agent orchestration patterns
  • Usage examples and best practices

🎯 Custom Skills

13 specialized skills for domain expertise and Claude Code customization, organized in two categories:

  • Claude Code Customization (6): Create skills, subagents, commands, hooks, plugins, and MCP server connections
  • Domain Expertise (7): WebGL, protein visualization, Argus deployment, NotebookLM, secure search, financial analysis, and financial modeling

📖 See skills/README.md for full skill catalog, usage guides, and creation instructions.

💬 Slash Commands

13 command templates across GitHub workflows, planning, research, and content generation.

📖 See commands/README.md for detailed documentation, examples, and workflow phases.

🔄 Multi-Agent Orchestration Patterns

Pattern Description Example
Sequential Agents execute in sequence, passing context forward backend-architect → frontend-developer → test-automator
Parallel Multiple agents work simultaneously performance-engineer + database-optimizer → Merged analysis
Validation Primary work followed by specialized review payment-integration → security-auditor → Validated
Conditional Dynamic agent selection based on analysis debugger → [backend-architect | frontend-developer]

See agents/README.md for orchestration patterns and workflow examples.

Directory Structure

.claude/
├── CLAUDE.md              # Repository guidance for Claude Code
├── README.md              # This file
├── settings.json          # Claude Code settings
├── .gitignore            # Git configuration
│
├── commands/             # Slash command templates (13 commands)
│   ├── README.md         # Command documentation
│   ├── issue.md          # Multi-phase issue creation workflow
│   ├── pr.md             # Comprehensive PR creation workflow
│   ├── user-story.md     # BDD user story with Gherkin syntax
│   ├── task.md           # Task orchestration with agents
│   ├── work-on-opens.md  # Priority board epic resolution
│   ├── merge-and-test.md # Merge plan executor with Chrome MCP
│   ├── architecture.md   # Architecture definition workflow
│   ├── mvp-requirements.md # MVP requirements definition
│   ├── todos.md          # Todo tracking with orchestration
│   ├── nlm-research.md   # NotebookLM research automation
│   ├── prompt.md         # Prompt engineering assistant
│   └── tiktok-tech.md    # TikTok tech content creation
│
├── skills/              # Custom skills (10 skills)
│   ├── Claude Code Customization/
│   │   ├── create-skill/           # Skill creation workflow
│   │   ├── create-subagent/        # Subagent builder
│   │   ├── create-command/         # Command generator
│   │   ├── create-hooks/           # Hook configurator
│   │   ├── create-claude-plugin/   # Plugin packager
│   │   └── connect-mcp-server/     # MCP integration
│   │
│   └── Domain Expertise/
│       ├── webgl-expert/           # WebGL & 3D graphics
│       ├── secure-web-search/      # Privacy-focused search
│       ├── analyzing-financial-statements/  # Financial ratios
│       └── creating-financial-models/       # DCF & valuation
│
├── agents/              # Specialized AI subagents (83+)
│   ├── README.md        # Agent documentation and usage guide
│   ├── [language]-pro.md    # Language-specific agents
│   ├── [domain]-[role].md   # Domain-specific specialists
│   └── examples/        # Usage examples and patterns
│
├── templates/           # GitHub templates
│   ├── GH_PR_TEMPLATE.md         # Standard PR template
│   ├── GH_PARENT_ISSUE_TEMPLATE.md  # Parent issue/epic
│   ├── GH_SUB_ISSUE_TEMPLATE.md  # Sub-issue template
│   └── GH_USER_STORY_TEMPLATE.md # BDD user story template
│
├── projects/            # Session histories (.jsonl)
├── shell-snapshots/     # Shell session persistence
├── todos/              # Task tracking files (.json)
├── statsig/            # Analytics cache
├── plugins/            # Claude Code plugins
│   ├── installed_plugins.json
│   ├── known_marketplaces.json
│   └── marketplaces/
└── ide/                # IDE integration

Quick Start

Installation

cd ~/.claude
git clone --recurse-submodules git@github.com:ronnycoding/.claude.git .

The configuration loads automatically when using Claude Code.

Usage

# Development Methodologies
/issue "Add user authentication feature"     # Individual: Issue-Driven
/user-story                                   # Behavioral: BDD (then /issue -> /task)
/work-on-opens <board-url>                    # Scaled: Epic-Driven (Agent Teams)

# Task Execution
/task #123                                    # Orchestrate agents on a sub-issue
/pr                                           # Create PR with convention analysis

# Planning & Architecture
/architecture "E-commerce Platform"           # Define tech stack and architecture
/mvp-requirements --idea="Task manager app"   # Define MVP scope and requirements

# Testing
/merge-and-test #123                          # Execute merge plan with Chrome MCP tests

# Research & Content
/nlm-research project="Market Analysis" type="competitive-intel"
/prompt task="Generate API documentation" format="markdown"
/tiktok-tech "Latest AI developments in 2025"

See commands/README.md for detailed command documentation, workflow phases, and examples.

Configuration

  • Tracked in git: commands/, templates/, skills/, README.md, CLAUDE.md
  • Ignored: agents/, plugins/, settings.json, session data, analytics

Session data (project context, shell history, todo state) persists locally across Claude Code invocations.

Resources

License

MIT License - Personal configuration repository for Claude Code.

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Claude Code configuration featuring specialized AI agents, custom skills, automated GitHub workflows with Epic/Task decomposition, and multi-agent orchestration patterns for complex software development

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