Help us improve
Share bugs, ideas, or general feedback.
Share bugs, ideas, or general feedback.
Share bugs, ideas, or general feedback.
By SteveGJones
Delegate SDLC tasks to 14 specialized AI agents that design multi-agent architectures, engineer prompts and RAG systems, orchestrate workflows, optimize LLM infrastructure, handle AI DevOps, testing, and quality assurance for production-ready AI/ML applications.
npx claudepluginhub stevegjones/ai-first-sdlc-practices --plugin sdlc-team-aiExpert in LangChain 0.1+ and LangGraph architectures. Use for LCEL chain design, RAG system architecture, multi-agent orchestration, tool integration patterns, or production deployment of LLM applications with observability.
Expert in MCP specification compliance, security auditing, and production readiness assessment. Use for quality reviews, security assessments, and deployment validation of MCP servers.
Expert in multi-agent system architecture including MCP and A2A protocols, inter-agent messaging, orchestration patterns, fault tolerance, and scaling strategies. Use for designing agent communication, orchestrating workflows, integrating heteroge...
Expert in agent architecture, persona design, and multi-agent systems. Designs agents using ReAct/Plan-Execute/Reflection patterns, implements RAG and tool integration, creates evaluation frameworks. Use for agent design decisions, system architec...
Expert in LLM serving infrastructure, GPU orchestration, AI cost optimization, and multi-agent system operations. Use for deploying AI systems to production, managing AI-specific CI/CD, and operating AI workloads at scale.
Uses power tools
Uses Bash, Write, or Edit tools
Share bugs, ideas, or general feedback.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Cross-cutting specialist agents — architects, researchers, performance engineers
Complete SDLC framework with 58 specialized agents for software development lifecycle management. Phase-based workflows (Inception→Elaboration→Construction→Transition), security reviews, testing orchestration, and deployment automation.
Make AI coding agents follow a repeatable engineering workflow with memory, verification, skills, and multi-agent setup
SDLC enforcement for AI agents — TDD, planning, self-review, CI shepherd
Plugin de ingeniería de software completa: 10 agentes de núcleo y 9 opcionales con personalidad propia, memoria persistente por proyecto, quality gates y flujos automatizados desde la idea hasta producción.
Self-orchestrating multi-agent development system — 8 specialized AI agents, parallel quality gates, and automated workflows. You say WHAT, the AI decides HOW.
Full-stack agents — frontend, backend, API, DevOps architects
Cloud infrastructure agents — cloud, container, SRE specialists
AI-First SDLC — zero-debt development with validators, enforcement, and workflows
Python-specific validation, patterns, and expert agents
Project management agents — agile coach, delivery manager, progress tracking
Table of Contents
A framework for integrating AI agents as primary developers while maintaining quality and process compliance. Provides specialist agents, validation tools, enforcement rules, and workflow skills for zero-technical-debt development.
Install the plugin family from the Claude Code marketplace. This is the standard approach for using the framework in your projects.
Step 1: Add the marketplace and install the core plugin
/plugin marketplace add SteveGJones/ai-first-sdlc-practices
/plugin install sdlc-core@ai-first-sdlc
Step 2: Configure your team
/sdlc-core:setup-team
This presents project types (Full-stack, AI/ML, Cloud, API, Security, Custom) and installs the matching team plugins. For example, a full-stack web app installs:
| Plugin | Description |
|---|---|
sdlc-core | Rules, validators, enforcement, workflows (always installed) |
sdlc-team-common | Solution architect, research agent, performance engineer, database architect |
sdlc-team-fullstack | Frontend, backend, API, DevOps architects |
Step 3: Start working
/sdlc-core:new-feature 1 my-feature "Description of the feature"
| Plugin | Description |
|---|---|
sdlc-core | Rules, validators, enforcement, workflows (always install) |
sdlc-team-common | Cross-cutting architects, researchers, performance engineers |
sdlc-team-ai | AI/ML specialists — architects, prompt engineers, RAG designers |
sdlc-team-fullstack | Frontend, backend, API, DevOps architects |
sdlc-team-cloud | Cloud, container, SRE specialists |
sdlc-team-security | Security, compliance, privacy specialists |
sdlc-team-pm | Agile coach, delivery manager, progress tracking |
sdlc-team-docs | Technical writer, documentation architect |
sdlc-lang-python | Python-specific validation, patterns, expert agent |
sdlc-lang-javascript | JavaScript/TypeScript validation and patterns |
| Skill | Description |
|---|---|
/sdlc-core:validate | Run validation pipeline (--syntax, --quick, --pre-push) |
/sdlc-core:new-feature | Create feature proposal, retrospective, and branch |
/sdlc-core:commit | Validated commit with test execution |
/sdlc-core:pr | Full validation + PR creation |
/sdlc-core:setup-team | Configure team formation for your project |
/sdlc-core:setup-ci | Generate GitHub Actions workflow |
/sdlc-core:release-plugin | Package source into plugins |
For testing unreleased agents or contributing to the framework, install agents directly from the repository. This approach gives you access to agents before they're published as plugins.
# Download the setup orchestrator
curl -s https://raw.githubusercontent.com/SteveGJones/ai-first-sdlc-practices/main/agents/v3-setup-orchestrator.md > v3-setup-orchestrator.md
mkdir -p .claude/agents && mv v3-setup-orchestrator.md .claude/agents/
# Restart Claude Code, then:
# "Use the v3-setup-orchestrator agent to set up AI-First SDLC for my project"
The orchestrator interviews you about your project and downloads only the agents you need. Use this approach when:
The original Python script approach. Still functional but superseded by the plugin system.