By TaylorHuston
Orchestrates an AI-assisted development workflow with specialized agents for planning, coding, testing, documentation, and code review. Manages tasks, specs, git operations, Jira integration, and release automation through structured commands and multi-agent coordination.
Create Architecture Decision Records through interactive conversation
Get implementation guidance for a phase without automated code generation - user implements manually
Unified branch operations with git-workflow enforcement
Check if CHANGELOG is up to date and update as necessary
Create a proper git commit with quality checks and conventional message
**AUTOMATICALLY INVOKED for AI and LLm related design and implementation.** Expert AI researcher and practitioner with deep knowledge of Large Language Models, their architectures, capabilities, and practical applications. Use for AI/ML technology selection, model comparison, context management strategies, prompt engineering, RAG systems, and emerging AI trends. Provides authoritative guidance on AI integration, implementation patterns, and optimization.
**AUTOMATICALLY INVOKED for API design and implementation tasks.** Design REST APIs, GraphQL schemas, service interfaces, and data validation patterns. **Auto-invoked when** designing endpoints, defining API contracts, or planning service integrations. Focus on developer experience, consistency, and robust error handling.
**AUTOMATICALLY INVOKED for AWS cloud architecture and implementation.** Expert AWS Solutions Architect with deep knowledge of AWS services, architectures, and best practices. Use for AWS service selection, architecture design, cost optimization, security best practices, and implementation guidance. Provides authoritative guidance on AWS-specific solutions and patterns.
**AUTOMATICALLY INVOKED for Azure cloud architecture and implementation.** Expert Azure Solutions Architect with deep knowledge of Microsoft Azure services, architectures, and best practices. Use for Azure service selection, architecture design, cost optimization, security best practices, and implementation guidance. Provides authoritative guidance on Azure-specific solutions and patterns.
**AUTOMATICALLY INVOKED for server-side implementation tasks.** Expert-level backend specialist for implementing robust, scalable server-side applications. **Use immediately when** building APIs, implementing business logic, setting up authentication, real-time features, or background processing.
Uses power tools
Uses Bash, Write, or Edit tools
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A lightweight, fully customizable framework to enable Spec Driven Development with Claude Code.
This is very much an alpha/experiment at the moment. Look at the commit history to see that for yourself. Right now it's a lot of throwing lots of things at the wall, seeing what works, seeing what doesn't, and frequently changing things as I use it in more "real world" scenarios.
/toolkit-init# 1. Add this marketplace
/plugin marketplace add TaylorHuston/ai-toolkit
# 2. Install the AI Toolkit plugin
/plugin install ai-toolkit
# 3. Initialize your project
cd your-project
/toolkit-init
# 4. Start developing
/project-brief
Run /toolkit-init --dry-run after plugin updates to preview template changes. See GETTING-STARTED.md in your initialized project for detailed update workflows.
/project-brief - Create your Project Brief through an interactive session. This is the high-level, relatively non-technical overview of your project: the problem you're solving, your target audience, core features. Think "One Pager" or "Elevator Pitch" - the guiding direction for everything else.
/adr - Create Architecture Decision Records for technical decisions ("What database should I use?", "Deploy to Vercel or Netlify?"). ADRs are intended to be static once approved - if something needs changing, create a new ADR to supersede it.
/spec - Create a Spec Document (the heart of the workflow). A Spec is a concrete plan for a body of related work - similar in scope to an Epic in Agile or a PRD. Written for easy parsing by Claude Code with clear requirements, acceptance criteria, and definition of done. Unlike ADRs, Specs are living documents that adapt as you discover hiccups and make adjustments during implementation.
/plan ### - Create a thorough implementation plan with all steps needed to complete the task. In the default configuration, this breaks work into discrete Phases (each a logical commit with strict TDD workflow), but you can customize to your specifications. Works for Tasks, Bugs and Spikes.
/implement ### PHASE - Start implementing. Execute a single phase or let Claude complete the entire task end-to-end. Quality gates and workflows make autonomous execution both possible and safe, but you're always in control.
| Command | Purpose |
|---|---|
/project-brief | Create/update project vision through interactive Q&A |
/spec | Create feature specs with acceptance criteria |
/adr | Make architecture decisions (Quick Mode or Deep Mode) |
/plan ### | Break down tasks into test-first implementation phases |
/implement ### PHASE | Execute phases with specialized agents and quality gates |
/quality | Multi-dimensional quality assessment before merge |
Full reference: See docs/development/misc/commands.md after running /toolkit-init.
Domain experts that auto-activate based on task context:
File-based project management - no external tools required. Note: This approach works best for individuals and small teams. For larger teams, consider Jira or some other dedicated project management tool. More integrations planned.
pm/
├── specs/
│ └── SPEC-001-user-auth.md # Feature spec with tasks list
│
└── issues/
├── 001-login-form/
│ ├── TASK.md # Requirements & acceptance criteria
│ ├── PLAN.md # AI implementation phases
│ └── WORKLOG.md # Implementation history
│
└── 002-session-timeout/
├── BUG.md
├── PLAN.md
└── WORKLOG.md
Benefits: Fully local, version controlled, AI-optimized file structure, customizable templates, no vendor lock-in.
Cons: No real-time team sync (commits only), may not scale to thousands of tasks without archiving.
Jira Integration (optional): Import issues (/jira-import), promote local work to Jira (/jira-promote), add AI comments (/jira-comment). See docs/development/misc/jira-integration.md after /toolkit-init.
npx claudepluginhub taylorhuston/ai-toolkitHarness-native ECC operator layer - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification
Complete developer toolkit for Claude Code